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The History of Auger Spectroscopy

Statistics and Chemometrics for Clinical Data Reporting, Part I

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This Month in Wavelength:

Raman Spectroscopy

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Columns

THE BASELINE

14

Auger SpectroscopyColumnist David W. Ball discusses Auger spectroscopy, in which the energies of emitted

electrons are measured.

David W. Ball

CHEMOMETRICS IN SPECTROSCOPY

18

Statistics and Chemometrics for Clinical Data Reporting, Part IThis article describes the application of chemometric methods and statistics for reporting

clinical quantitative measurement methods. Part I will introduce these concepts and Part II

will discuss the statistical underpinnings in greater detail.

Jerome Workman, Jr. and Howard Mark

FOCUS ON QUALITY

22

Understanding and Interpreting the New GAMP 5 Software CategoriesThe GAMP (Good Automated Manufacturing Practice) guide version 5 was released in March

2008 and one of the changes was that the classification of software was revised — again.

In this column we will look at what the changes mean for the laboratory and whether all of

these should be implemented.

R.D. McDowall

ArticlesCurrent Status of Standoff LIBS Security Applications at the United States Army Research Laboratory 32The U.S. Army Research Laboratory (ARL) has been applying standoff laser-induced

breakdown spectroscopy (LIBS) to hazardous material detection and determination. Here,

the authors describe several standoff systems that have been developed.

Frank C. De Lucia, Jr., Jennifer L. Gottfried, Chase A. Munson, and Andrzej W. Miziolek

Spectral Studies on the Interaction of [Ru(bpy)2(BTIP)]2+ with DNA and Determination of Nucleic Acids at Nanogram Levels 39The interaction of [Ru(2, 2’-bipyridine)2(2-benzo[b] thien-2-yl-1H-imidazo[4,5-f][1,10]

phenanthroline)]2+ ([Ru(bpy)2(BTIP)]2+) with nucleic acids in weak acidic medium is studied

based upon the measurements of resonance light scattering (RLS) and UV–vis absorbance.

Chao Weng, Xiaoming Chen, and Changqun Cai


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Ramon M. Barnes University of Massachusetts

Paul N. Bourassa Lifeblood

Chris W. Brown University of Rhode Island

Kenneth L. Busch National Science Foundation

Ashok L. Cholli University of Massachusetts at Lowell

David M. Coleman Wayne State University

Patricia B. Coleman Ford Motor Company

Bruce Hudson Syracuse University

Kathryn S. Kalasinsky Armed Forces Institute of Pathology

David Lankin University of Illinois at Chicago, College of Pharmacy

Barbara S. Larsen DuPont Central Research and Development

Ian R. Lewis Kaiser Optical Systems

Steve Lowry ThermoFisher Scientific

Howard Mark Mark Electronics

R.D. McDowall McDowall Consulting

Linda Baine McGown Rensselaer Polytechnic Institute

Robert G. Messerschmidt Rare Light, Inc.

Nancy Miller-Ihli M–I Research

Francis M. Mirabella Jr. Equistar Technology Center

John Monti Shimadzu Scientific Instruments

Thomas M. Niemczyk University of New Mexico

Anthony J. Nip CambridgeSoft Corp.

John W. Olesik The Ohio State University

Richard J. Saykally University of California, Berkeley

Basil I. Swanson Los Alamos National Laboratory

Jerome Workman Jr. Luminous Medical, Inc.

Contributing Editors:

Fran Adar Horiba Jobin YvonDavid W. Ball Cleveland State UniversityKenneth L. Busch National Science Foundation John Coates Coates ConsultingHoward Mark Mark ElectronicsVolker Thomsen ConsultantJerome Workman Jr. Luminous Medical, Inc.

Process Analysis Advisory Panel: James M. Brown Exxon Research and Engineering Company Bruce Buchanan Sensors-2-InformationLloyd W. Burgess CPAC, University of WashingtonJames Rydzak Glaxo SmithKlineRobert E. Sherman CIRCOR Instrumentation Technologies John Steichen DuPont Central Research and Development D. Warren Vidrine Vidrine Consulting

European Regional Editors: John M. Chalmers VSConsulting, United Kingdom David A.C. Compton Industrial Chemicals Ltd.

Editorial Advisory Board

Spectroscopy ’s Editorial Advisory Board is a group of distinguished individuals

assembled to help the publication fulfill its editorial mission to promote the effective

use of spectroscopic technology as a practical research and measurement tool.

With recognized expertise in a wide range of technique and application areas, board

members perform a range of functions, such as reviewing manuscripts, suggesting

authors and topics for coverage, and providing the editor with general direction and

feedback. We are indebted to these scientists for their contributions to the publication

and to the spectroscopy community as a whole.



Market Profile: Raman Enabled FT-IR

IndustryThe Space Shuttle Atlantis recently visited the Hubble

Space Telescope for the first time since 2002. During one

of the mission’s five spacewalks, astronauts swapped the

Corrective Optics Space Telescope Axial Replacement

(COSTAR) device for the new Cosmic Origins Spectrograph

(COS). COSTAR is the device that corrected Hubble’s

original blurred vision during the first servicing mission.

Since all of Hubble’s instruments after that point were

designed with the correction built in, COSTAR is no longer

needed. COS is designed for ultraviolet spectroscopy and

will be used to study such areas as galaxy evolution, the

formation of planets and the rise of the elements needed

for life, and the cosmic web of gas between galaxies. For

more information, visit www.spectroscopyonline.com.

ResearchUsing nuclear magnetic resonance (NMR), researchers

from the Agricultural Research Service, New England

Plant, Soil, and Water Laboratory (Orono, Maine)

showed that conventional and organic dairy manures from

commercial dairy farms differed in concentrations of plant

nutrients, including phosphorus, metals, and minerals.

They used both solution NMR spectroscopy and solid-state

NMR spectroscopy to pinpoint these differences. Solution

NMR spectroscopy is a widely used method for analyzing

phosphorus content in manure, but the added use of

solid-state NMR enabled the researchers to detect at least

17 different chemical forms of phosphorus that varied

in concentration. The team found that the organic dairy

manure contained higher levels of phosphorus, calcium,

potassium, manganese, zinc, and magnesium than the

conventional manure. In addition, the organic manure had

more types of phosphorus that are comparatively slow to

dissolve. If the organic manure were used as fertilizer, this

slow-release characteristic would increase the likelihood

that the nutrients would be taken up by crops rather than

being washed out of fields and into groundwater sources.

Researchers at the Harvard-Smithsonian Center for

Astrophysics (Cambridge, Massachusetts) have created

an “astro-comb” to help astronomers detect lighter, Earth-

like planets around distant stars. In most cases, extrasolar

planets are detected through spectroscopy, which reveals the

chemical composition of the star and, through the Doppler

effect, can also indicate the presence of a planet orbiting

the star. Current spectroscopy techniques can determine

star movements to within a few meters per second. Using

their new method, the Harvard researchers were able to

News Spectrum

Infrared (IR) spectroscopy and Raman spectroscopy are very complementary methods. The strongest demand tends to come from applications that require analytical information from a potentially broad range of compounds and functional groups. The global market for combined Raman and FT-IR accounts for a small but growing percentage of both the broader IR and Raman spectroscopy markets.

Compounds and functional groups that give strong signatures in IR spectroscopy, such as carbonyls and nitriles, tend to be weak in Raman spectra, while the converse generally is true for aromatic compounds. While FT-IR spectroscopy has been a well-established laboratory analytical technique for some time, viable and effective Raman spectroscopy instrumentation has developed fairly recently due to the breaking of various technological barriers. Now that both techniques are fairly well established, most vendors in these areas now offer some form of combined configuration of the techniques.

Demand from the pharmaceutical industry is the largest for combined FT-IR and Raman spectroscopy because many drug formulations combine compounds

and functional groups that generate strong spectra in one technique or the other. This makes combined Raman and FT-IR very useful in drug development. A significant and rapidly growing segment of this market is forensics, which

is generally accounted for by government laboratories. The combination of Raman and FT-IR makes it possible to screen for a very wide range of unknown compounds, and to do so without disturbing the sample.

Most of the market for combined Raman and FT-IR consists of Raman add-on modules for FT-IR instrumentation, although fully

integrated instruments are available. SDi estimates the combined global demand for these products to be around $25 million in 2009, and it should see steady high single-digit growth, even in the face of the current recession.

The foregoing data were based on SDi’s market analysis and perspectives report entitled Global Assessment

Report, 10th Edition: The Laboratory Life Science and

Analytical Instrument Industry, September 2008. For more information, contact Stuart Press, Vice President — Strategic Analysis, Strategic Directions International, Inc., www.strategic-directions.com.

Raman enabled FT-IR market demand — 2009.

Other18%

Polymers12%

Government22%

Pharma31%

Academic17%

June 2009 Spectroscopy 24(6) 13www.spec t roscopyonl ine .comwww.spec t roscopyonl ine .com

calculate star velocity shifts of less than

1 m/s, which allows them to pinpoint a

planet’s location more accurately.

The method involves the use of a

frequency comb as the basis for the

astro-comb. With this technique, a

special laser system is used to emit

light at a series of frequencies that are

evenly spaced across a wide range

of values. A plot of these energy

components would look like the teeth

of a comb, which is where the method

gets its name. The energy of these

comblike laser pulses can be used to

calibrate the energy of light coming in

from a distant star, which essentially

sharpens the planet-detection process.

The Harvard group was scheduled

to present their results at the 2009

Conference on Lasers and Electro

Optics/International Quantum

Electronics Conference (CLEO/IQEC)

on June 1, 2009, in Baltimore.

Using near-infrared spectroscopy

(NIR), a researcher at Queensland

University of Technology (Brisbane,

Australia) has developed a way

to analyze human hair for forensic

purposes. Using this technique,

scientists can analyze hair even after

immersion in water, making it a useful

tool for identifying victims of a tsunami.

Unlike DNA, human hair can survive

in relatively harsh environments. This

technique makes it possible to obtain

the infrared profile from only a tiny part

of a strand of hair and then interpret

the profile using mathematical methods

that compare it with similar profiles

collected from reference hair samples.

The technique can establish a person’s

gender, race, and whether they had

chemically treated their hair (as well as

what the original hair color was). The

availability of portable NIR instruments

enables this technique to be used at

crime scenes and disaster scenes.

EducationAs recently reported in the Bangor Daily

News (Bangor, Maine), Bangor High

School senior Anne Marie Lausier will

travel to Anchorage, Alaska, to compete

in an international competition known

as the Stockholm Junior Water Prize. Ms.

Lausier has spent most of her free time

over the last several months conducting

Ph.D.-level research with synchronous-

scan fluorescence spectroscopy (SFS)

to detect pharmaceuticals and personal

care products in the waters of three

area lakes. SFS measures electron

activity to test water samples without

separating particles from the water.

To Ms. Lausier’s knowledge, no other

local researchers have used SFS for this

purpose.

Mitchell Dobberpuhl, a senior at

South Dakota State University (Sioux

Falls), was recently one of 10 students

to receive Schultz-Werth Awards for

his research paper on the use of solid-

phase microextraction and GC–MS to

detect sulfur mustard. According to

Dobberpuhl, current methods for the

detection of sulfur mustard (a chemical

warfare agent) lack sensitivity and must

be performed soon after exposure

because the body metabolizes sulfur

mustard to thiodiglycol (TDG) rapidly.

Dobberpuhl’s use of solid-phase

microextraction showed a substantial

increase in sensitivity compared to

the more common method of direct

injection analysis. ◾

The Baseline

Auger SpectroscopySome forms of spectroscopy involve actions other than measuring a property of light. In the case of this form of spectroscopy, the energies of emitted electrons are measured.

David W. Ball

In 1923, Austrian physicist Lise Meitner reported on the emission of electrons from atoms that were bombarded with other electrons (1). In 1925, French physicist

Pierre Auger reported the same effect, and correctly attrib-uted it to an excited-state atom that is giving off energy by releasing an upper-quantum state electron (2). Because the energies of these electrons are element-specific, it forms the basis of a type of spectroscopy. Auger spectroscopy was born. (Once again, Meitner is shut out. Many science historians believe that she should have shared Otto Hahn’s 1944 Nobel Prize in chemistry for the discovery of nuclear fission. The use of “Auger” to name this form of spectros-copy is thus another example of an instance where Meitner might not be getting the credit she deserves.)

Auger (pronounced “oh-ZHAY”) spectroscopy can be considered a form of X-ray photoelectron spectroscopy (XPS) (3), at least in one of its guises. For about 30 years after its formal discovery, it actually was thought of as a nuisance in the performance of XPS. However, since the 1950s, technology has advanced to be able to take advan-tage of the effect as a separate spectroscopic technique (4).

How It Works

Figure 1 shows a schematic of the steps involved in the Auger effect. In step (a), an incoming energy source, which can be a beam of electrons or X-rays, knocks out a core electron (that is, an electron relatively close to the atomic nucleus). This creates an electronically excited atom, as indicated by the asterisk in the right side of step (a). Step (b) shows that an electron from an outer orbital moves down to fill the hole that the first electron left. This still leaves the atom electronically excited. The atom loses this additional energy by emitting an X-ray photon or ejecting an electron from an even higher shell with whatever excess kinetic energy is necessary, leaving behind a (temporarily) 2+-charged ion, as shown in step (c).

The kinetic energy of the ejected electron, KE, can be approximated by the energy levels of the original elec-trons involved:

KE = E1 − E2 − E3

where E1, E2, and E3 are the original energies of the first core electron, the second electron that moves down, and the third electron that gets ejected, respectively. There is supposed to be a correction for the third energy, because it is actually an energy of the ion, not the atom, but these are usually ignored. Because three electrons are involved, Auger spectroscopy is not used to detect hydrogen or he-lium but can be utilized for any heavier element. Because the energy levels of the elements typically are well-known, Auger spectroscopy can be used to determine the elemen-tal analysis of a sample, either by analysis of the X-rays

Figure 1: The steps in the Auger spectroscopy process.


emitted or the electrons emitted. Because a free electron will not travel a large distance in a solid, Auger spectroscopy is largely a surface tech-nique, allowing users to probe the chemical composition of a surface.

As the atomic number of the ele-ments being analyzed increases, so

do the number of possible transitions, suggesting that Auger spectra get hopelessly complicated for larger ele-ments. However, experience shows that most elements show only a few strong signals in their Auger spectra, domi-nated by transitions between the 1s, 2s, 2p, and occasionally the n = 3 shells.

Instrumentation

Because Auger spectroscopy is sensi-tive to the outer layers of a sample, samples must either be very clean or (more usually) be cleaned and then examined in an ultrahigh vacuum (<10−9 mm Hg pressure) environ-ment. Schematically, an Auger spectrometer is straightforward. An electron gun utilizing a tungsten fila-ment, a lanthanum hexaboride tip, or a field emission gun source is aimed at the sample and emits electrons. Electron focusing optics might also be present, especially for techniques that attempt a fine spatial resolution of the sample. The Auger electrons are de-tected by an electron detector, which scans the kinetic energies of the elec-trons and records output to a detector. Figure 2 shows a diagram of a modern Auger spectrometer. To measure the number of electrons emitted versus Figure 2: Schematic of an Auger spectrometer.

Figure 3: Auger spectra of a nickel–aluminum film (top) that has been oxidized (middle) and then exposed to barium vapors (bottom). Note how

the signals specific to each additional element appear in the Auger spectrum after each treatment. (Adapted from reference 5.)


David W. Ball is a

professor of chemistry at

Cleveland State Univer-

sity in Ohio. Many of his

“Baseline” columns have

been reprinted in book

form by SPIE Press as The

Basics of Spectroscopy, available through

the SPIE Web Bookstore at www.spie.org.

His most recent book, Field Guide to Spec-

troscopy (published in May 2006), is avail-

able from SPIE Press. He can be reached

at [email protected]; his website is

academic.csuohio.edu/ball.

For more information on this topic, please visit:

www.spectroscopyonline.com/ball

their kinetic energy, most Auger spec-trometers use a cylindrical mirror an-alyzer (represented by the darker lines in Figure 2) that varies a potential on some concentric cylindrical elec-trodes, the inner of which has several apertures in it to allow only electrons of a certain kinetic energy to pass at any certain voltage.

Because many electrons, not just Auger electrons, are scattered as part of a measurement, the electron detec-tor actually detects a nonzero back-ground noise level. Auger spectra are plotted in derivative mode, much like electron spin resonance (ESR) spectra, to better emphasize the slight variations in the signal. Some spectra are also mathematically manipulated to emphasize the Auger signal over the background noise, for example by multiplying the energy of the Auger electron by its intensity and then tak-ing the derivative. A typical Auger spectrum is shown in Figure 3.

According to Losito and colleagues (6), Auger spectra also can be recorded using two different X-ray excitation

sources. Because the kinetic energies of the ejected electrons are not depen-dent upon the energy of the excitation source, Auger spectra will have the same relative spacing of lines and can be recognized easily by superimposing the two different spectra.

Auger spectroscopy was once con-sidered a nuisance in X-ray photo-electron spectroscopy. In the past 50 years, however, it has developed into a useful technique in its own right, pro-viding important surface information for scientists and engineers exploring a wide range of interfacial science.

References

(1) L. Meitner, Z. Physik 17, 54 (1923).

(2) P. Auger, Compt. Rend. 180, 65 (1925).

(3) D.W. Ball, Spectroscopy 18(11), 36

(2003).

(4) D.F. Stein, Chapter 1 in Auger Electron

Spectroscopy, C.L. Briant and R.P.

Messmer, Eds. Treatise on Materials

Science and Technology, vol. 30 (Aca-

demic Press, Boston, 1988).

(5) E. Ozensoy, C.H.F. Peden, and J. Szanyi, J.

Phys. Chem. B 110, 17001–17008 (2006).

(6) I. Losito, L. Sabbatini, and J.A.

Gardella Jr., Chapter 16 in Compre-

hensive Desk Reference of Polymer

Characterization and Analysis, R.F

Brady, Jr., Ed. (Oxford University Press,

Oxford, 2003).

June 2009 Spectroscopy 24(6) 17www.spec t roscopyonl ine .com

Often there is confusion in multidisciplinary uses of statistical methods due to the variation in termi-nology, assumptions, and specific use of statistical

methods within each scientific or technical discipline. An analytical chemist might look at analytical performance quite differently than a clinical chemist, or a physicist, or a mechanical engineer. An individual from one techni-cal discipline might only be interested in overall error or deviation of one analysis method as compared to another reference method, whereas another individual might be more interested in bias and precision, and still another in tolerance stacking.

In the interest of unification of multiple disciplines into a reasonable set of statistical parameters useful for analytical data evaluation, a group of individuals at Luminous Medi-cal, Inc. (Carlsbad, California) decided to consolidate their efforts and combine analytical chemistry, clinical chemistry, bioengineering, physics, and biochemistry concepts into a single set of statistical parameters that would be useful and descriptive to a multidisciplinary team involved in looking at analytical method comparison (please refer to Acknowl-edgment section).

This column describes how to perform statistical analy-sis of quantitative measurement methods. The equations and terminology in this article are consistent with Clini-cal and Laboratory Standards Institute (CLSI) guidelines (1). These statistical analyses evaluate the accuracy of a

test method compared to a reference method that mea-sures the same analyte. References 2–6 yield multiple descriptions and worked problems associated with the individual statistics demonstrated in this article.

Definitions

A comparison of methods records differences between a test method and a comparative or reference method:

X comparative or reference methodY test methodxi observation i from comparative methodyi observation i from test methodFor clarity, this article assumes the comparative

method is a traceable reference method that has better precision than the test method, which can be achieved by averaging replicate reference measurements if necessary.

Measurement Error

The Measurement Error (ei) is the test method measurement minus the reference method

e = Test Measurement − Reference Method

or equivalently, using the CLSI definitions, the measure-ment error for the for the ith observation is

ei = y

i − x

i

Chemometrics In Spectroscopy

Statistics and Chemometrics for Clinical Data Reporting, Part IThis article describes the application of chemometric methods and statistics for reporting clinical quantitative measurement methods. The equations and terminology are consistent with the Clinical and Laboratory Standards Institute (CLSI) guidelines. These chemometric and statistical methods describe the accuracy and precision of a test method compared to a reference method for a single analyte determination. Part I will introduce these concepts and Part II will discuss the statistical underpinnings in greater detail.

Jerome Workman, Jr. and Howard Mark


AccuracyAccuracy includes both random and systematic components of a single measurement. Accuracy for a group of observations of the test method relative to the comparative method is calculated as

n

e

Accuracy

n

i

i∑=

=1

2

[1]

where n is the number of measure-ments. A common statistical term for this accuracy calculation is a root mean squared error (RMSE). Similar statistics are used to quantify er-rors in multivariate calibration and prediction, such as the root mean squared error of prediction (RMSEP, also known as SEP). Note: SEP = the square root of (SDr

2 + Bias2).

Trueness and BiasTrueness is the closeness in agree-ment between the average value from a series of measurements and a recog-nized reference method or traceable standard. The measure of ‘trueness’ is usually expressed in terms of bias (B)

Bias = average (Test Measurement) − average (Reference Method)

or equivalently, using the CLSI defi-nitions

xyn

xy

B

n

i

n

i

ii

−=

−

=

∑ ∑= =1 1

[2]

For more details on bias estima-tion and verification see references 1 and 4–6.

PrecisionPrecision is defined as the closeness of the agreement between the test measurement results under speci-fied conditions. In general, medical device manufacturers report preci-sion estimates for repeatability and reproducibility conditions. These are considered the extreme measures of precision. Repeatability (within-run

precision) is the precision of mea-surements made by the same opera-tor, using the same equipment, in a short period of time. Reproducibility (total precision) is assessed over mul-tiple days and usually includes differ-ent operators and devices.

The simplest way to estimate repeatability is to compute the stan-dard deviation (SD) of a sequence of repeat measurements on identical test material

( )

1

1

2

2

−

=

∑∑

=

n

n

yy

SD

n

i

i

i

ityrepeatabil

[3]

In blood samples the glucose level can change due to red blood cell me-tabolism. If these glucose changes are a significant contribution to the standard deviation then repeatability can be approximated from the mea-surement errors

( )

1

1

2

2

−

−

≅

∑∑

=

n

n

ee

SD

n

i

i

i

ityrepeatabil

[4]

This is an approximation because the repeatability estimate now in-cludes the imprecision of both the test method and the reference method.

The reproducibility (ST) of a mea-surement is a calculation that typically combines repeatability, between-run, and between-day standard deviations. The necessary calculations are in-cluded in CLSI Document EP5-A2 (1).

Precision (expressed in terms of re-peatability and reproducibility) should be assessed at concentration levels that span the measuring range and include medical decision levels. The reported results should include the concentra-tion level, number of samples, and pre-cision. Precision should be reported in absolute units (such as mg/dL) and in relative units expressed as a coefficient of variation. The coefficient of varia-tion expresses the precision relative to the average reference value (x� i ).

ix

SDCV

100%

⋅

=

[5]

Sample Data CalculationsThe sample calculations use data from Reference 5 for comparison.

Computation of the Regression LinePearson Product-Moment Correlation Coefficient (r)The Pearson product-moment correla-tion coefficient for x and y data pairs

Table I: Data from reference 5 for sample calculations

Reference values Test values Measurement error Error squared

0 2.1 2.10 4.41

2 5 3.00 9.00

4 9 5.00 25.00

6 12.6 6.60 43.56

8 17.3 9.30 86.49

10 21 11.00 121.00

12 24.7 12.70 161.29

Statistical parameter Calculated value

Accuracy 8.02

Standard deviation 4.0390

Bias 7.10

% CV 67.3

Slope 1.930

Intercept 1.52

R 0.9989

r2 0.9978


is the alikeness of x to y including their respective differences ratioed to the dispersion (standard deviation) of the dataset. So the same error between x and y computes to a higher correla-tion when the data is more disperse or has a wider range. Therefore to com-pare correlation between experiments one should use the same data distribu-tion for both. A high correlation does not mean smaller error unless the spread of the data used in the experi-ments is equivalent.

The correlation coefficient com-puted using a standard summation notation is defined as:

r =

xi− x

i( ) ⋅ yi− y

i( )[ ]i =1

n

∑

xi− x

i( )2

⋅ yi− y

i( )2

i =1

n

∑i =1

n

∑

=

sum[(x− x ) ⋅ y− y ( ) ]

sum(x− x )2 ⋅ sum(y− y )2 [6]

Coefficient of Determination (R2) The Coefficient of Determination, R2, is the square of the Pearson prod-uct-moment correlation coefficient. This statistic represents the amount of variation in the data that is modeled by linear fit of the test and comparative data pairs as a fraction of 1.0.

Note: For a multivariate calibra-tion, this statistic is often termed the coefficient of multiple determina-tion. It specifically reports the total amount of variation in the data that is fully modeled by the calibration equation as a total fraction of 1.0. If the R2 is 1.00 then 100% of the varia-tion is modeled in the calibration; similarly, an R2 of 0.80 indicates 80% of the variation has been modeled using the mathematics selected.

Slope (m0)This is the slope of the regression line between x and y paired values. A slope of 1.00 indicates perfect agreement between a change in reference value magnitude and a change in test value magnitude. This slope value does not indicate the magnitude of the bias or of the intercept of the regression line between x and y values. It is computed as follows (summation notation is indicated):

m0 =

n ⋅ (yi ⋅ x i )− y i ⋅ x i

i =1

n

∑i =1

n

∑i =1

n

∑

n ⋅ y i

2( )− y i( )

2

i =1

n

∑i =1

n

∑

=

n ⋅ sum(y ⋅ x )−sum(y )−sum(x )

n ⋅ sum(y 2)−(sum y )2

[7]

y-Intercept (i)The y-intercept is the point on the y-axis where the regression line crosses the 0 reference (x) value. It is not the bias which has already been defined as Parameter #3. In summation notation the intercept is computed as follows:

i =

yi

2⋅ x

i− y

i⋅ (y

i⋅ x

i)

i =1

n

∑i =1

n

∑i =1

n

∑i =1

n

∑

n ⋅ y i

2

( )− (y i )2

i =1

n

∑i =1

n

∑

=

sum(y 2) ⋅ sum(x )−sum (y ) ⋅ sum(y ⋅ x )

n ⋅ sum(y 2)−(sum y )2 [8]

AcknowledgmentThe column editors would like to thank Drs. Bill Patterson, Shonn Hendee, Stephen Vanslyke, and David Abookasis of Luminous Medi-cal for their multidisciplinary con-tributions in authorship, review, and editing for this discussion of statisti-cal methods suitable for clinical data presentation when comparing differ-ent methods of analysis.

ReferencesMany references are available. These

have been selected as they are specifi-

cally related to the use of data for spec-

troscopy, and for comparison of general

analytical methods.

(1) Clinical and Laboratory Standards

Institute (CLSI) guidelines: Q300-001,

Terminology for standard definitions.

For more details on statistical estima-

tion and verification see:

• CLSI Document EP5-A2, Evaluation

of Precision Performance of Quan-

titative Measurement Methods; Ap-

proved Guideline — Second Edition.

• CLSI Document EP10-A2, Prelimi-

nary Evaluation of Quantitative Clini-

cal Laboratory Methods; Approved

Guideline — Second Edition.

• CLSI Document EP15-A2, User Verifi-

cation of Precision and Trueness; Ap-


• CLSI Document EP9-A2, Method

Comparison and Bias Estimation

using Patient Samples; Approved


• CLSI Document EP10-A2, Prelimi-

nary Evaluation of Quantitative Clini-

cal Laboratory Methods; Approved


• CLSI Document EP15-A2, User Verifi-

cation of Precision and Trueness; Ap-


(2) ASTM Standard Practice E1655-00,

“Standard Practices for Infrared,

Multivariate, Quantitative Analysis,”

American Society for Testing and Ma-

terials International, Barr Harbor Dr.,

West Conshohocken, PA 19428.

(3) N.M. Faber, F.H. Schreutelkamp, and

H.W. Vedder, Spectroscopy Europe

16(1), 17–20 (2004).

(4) W.J. Youden and E.H. Steiner, Statisti-

cal Manual of the AOAC, 1st Ed. (As-

sociation of Official Analytical Chem-

ists, Washington, D.C., 1975).

(5) J.C. Miller and J.N. Miller, Statistics for

Analytical Chemistry, 2nd Ed. (Ellis

Horwood, New York, 1992).

(6) H. Mark and J. Workman, Chemomet-

rics in Spectroscopy (Elsevier/Academic

Press, Boston, 2007), chapters 58–61.


www.spectroscopyonline.com/mark

Howard Mark serves

on the Editorial Advisory

Board of Spectroscopy

and runs a consulting

service, Mark Electronics

(Suffern, NY). He can be

reached via e-mail:

[email protected]

Jerome Workman,

Jr. serves on the Editorial

Advisory Board of

Spectroscopy and is

currently with Luminous

Medical, Inc., a company

dedicated to providing

automated glucose management systems

to empower health care professionals.


Focus On Quality

Understanding and Interpreting the New GAMP 5 Software Categories The GAMP (Good Automated Manufacturing Practice) guide version 5 was released in March 2008 and one of the changes was that the classification of software was revised — again. This column will look at what the changes mean for the laboratory and whether all of these should be implemented.

R.D. McDowall

Version 5 of the Good Automated Manufacturing Practice (GAMP) guide (1) was released last year. This publication has been available since 1994,

when version 1 was informally published in the UK, and since its inception it has always contained a classification of software. This is one of the best parts of the guide as it has an in-built risk assessment, as we shall see in this column. We will explore version 5 of the software classi-fication and see what changes we need to make to ensure that it can be implemented practically and effectively in any laboratory.

However, before we continue much further I should also declare a vested interest: I have a love–hate relation-ship with the GAMP guide. I love the classification of software outlined in Appendix M4 and hate the life cycle V model. My rationale for this position is that versions 1–4 of the guide presented a single life cycle V model that was really only applicable to process equipment and manufacturing systems. It had very little to do with com-puterized systems, especially laboratory ones. Therefore, every validation was shoehorned into an inappropriate model because there was little thought and intelligence

applied and the model followed blindly. For example, when a commercially available laboratory system was validated, functional and design specifications were writ-ten for virtually no gain but at a great cost in time and resources. The problem lay in the origins of the GAMP guide. The first version was written by a group of volun-teers in the UK in the early 1990s as a mechanism to con-trol suppliers of process equipment to the pharmaceutical industry, and this legacy survived through to version 4. However, the model does not make it into version 5 of the Guide, which is a shame; as mentioned above, the model is very good for process equipment.

However in GAMP version 5, I’m very pleased to say that the “one size fits all” approach has been replaced by a breath of fresh air with different life cycles depending on the classification of the software being implemented. The key message is that now a single size life cycle model does not fit all systems. Note that GAMP is a guide and you can deviate from it — all that is required is the appli-cation of thought and intelligence coupled with effective risk management that is well documented. OK, perhaps this is a step too far . . . .


Software Classification CategoriesAs I mentioned earlier, the software categories in GAMP 5 have been re-vised (1). To appreciate the scope of these changes fully we need to look at the classification of software from GAMP 4 (2) and compare this with GAMP 5, as shown in Table I.

In the beginning, or at least in GAMP 4, there were five categories of software:⦁ Category 1: Operating systems⦁ Category 2: Firmware⦁ Category 3: Standard software ⦁ Category 4: Configured software ⦁ Category 5: Custom softwareThe constituents of each category

are outlined in Table I; however, there was always a debate about some commercial software packages — were they category 3 or 4? Many spectroscopists would argue that an application should be classified as category 3 and not 4, as it should be less work to validate and evade the real classification. To help resolve this debate, in GAMP 5 the software

categories have been revised and refined — most for the better and one for the worse. This is a natural evolution of this approach to soft-ware classification. So we now have the following four categories:⦁ Category 1: Infrastructure

Software ⦁ Category 3: Nonconfigured

products⦁ Category 4: Configured products⦁ Category 5: Custom applications Refer to Table I as we discuss the

changes in the software classification in more detail in the next section.

Why Classify Software? Before we go into a detailed dis-cussion of the software categories, perhaps we should ask the question “Why bother to classify software?”. What benefit does this software clas-sification provide?

If you look at Table I there is a built-in risk assessment. The least risky and most widely available software is in category 1 (operating systems, databases, office software,

and other widely available software). This is widely available software that can be used by anyone and in any in-dustry. As we progress through down the categories as shown in Table I, generally the software becomes more specialized in its function (from a general office application to software that can control a spectrometer to ac-quire and process data then report the results). As we go down the list there is the increasing ability of the users to change the operation of software and process the results until we reach category 5. In category 5 is a unique solution that is conceived, specified, written, tested, and maintained by the users or the organization; here is the greatest risk. Let’s now take a detailed look at each of the software categories and see what has changed and if there are any problems we need to discuss.

Software Classification Changes and Their Laboratory Impact Presented and discussed here are the various changes to the software clas-sifications in the new GAMP guide.

Table I: Comparison of software categories in GAMP 4 and GAMP 5

GAMP 4 Software Categories GAMP 5 Software Categories

Category 1: Operating Systems• Operating systems only

Category 1: Infrastructure Software Greatly expanded scope to cover• Established or commercially available layered software including

operating systems, databases, offi ce applications, and so forth.• Infrastructure software tools including antivirus, network man-

agement tools, and so forth.

Category 2: Firmware• Confi gurable and nonconfi gurable fi rmware• Custom fi rmware is category 5

Category 2: Firmware• Discontinued — fi rmware now treated as category 3, 4, or 5.• Clash with USP <1058> over approach for Group B laboratory

instruments: validate or qualify?

Category 3: Standard Software Packages• Commercially available standard software packages.• Confi guration limited to establishing the run-time environment.

Category 3: Nonconfi gured Products• Off-the-shelf products that cannot be changed to match the

business processes.• Can also include products that are confi gurable but only the

default confi guration is used.

Category 4: Confi gurable Software Packages• Confi gurable software packages provide standard interfaces

and functions that enable confi guration of user-specifi c business or manufacturing process.

Category 4: Confi gured Products• Confi gured products provide standard interfaces and functions

that enable confi guration of the application to meet user-spe-cifi c business processes.

• Confi guration using a vendor-supplied scripting language should be handled as custom components (category 5).

Category 5: Custom (Bespoke) Software• These systems are developed to meet the specifi c needs of the

user company.

Category 5: Custom Applications• These applications are developed to meet the specifi c needs of

the regulated company.• Implicitly includes internal application macros, LIMS language

customizations, VBA spreadsheet macros.• High inherent risk with this type of software.


Category 1: Greatly Expanded Scope — Infrastructure Software Category 1 has undergone a radical change and expansion from simply operating systems, that had been constant in GAMP versions 1 to 4, to infrastructure software. This cat-egory is broken down into two sub-categories: ⦁ Established or commercially

available layered software and ⦁ Infrastructure software tools. The intention is that the infra-

structure software in this category provides the computing environ-ment for running both regulated and nonregulated applications within an organization. All software in this category needs to be controlled and qualified in an organization so that dual standards are not applied by the IT department, which can question the status of validated applications if not done.

Software in the subcategory of Established or Commercially Avail-able Layered Software still includes operating systems from GAMP 4, but this has also been expanded to encompass a greater scope:

⦁ databases ⦁ programming languages ⦁ middleware ⦁ office software⦁ ladder logic interpreters (for

manufacturing systems), ⦁ statistical programming tools

and spreadsheet packages. The key issue is that many of these

software tools are the base products for the applications used in the labo-ratory or they are the foundation layer for the laboratory applications to operate under. For example, if your spectrometer has application software that has a database to manage the methods, data, and results you gen-erate, the latter is configured by the spectrometer supplier from the out-of-the-box database to operate with their application software. Languages are used as a means to write the ap-plications, each of which will be vali-dated for their intended use.

Note that category 1 also includes office software such as word process-ing, spreadsheet, database, and pre-sentation applications. Now before you rush off thinking that Excel tem-plates and macros do not need to be

validated, think again, as the guide notes that “applications developed using these packages” are excluded from category 1 and these can be category 3, 4, or 5, respectively (1), depending on their complexity.

Note also the phrasing of the subcategory “established or com-mercially available”. This means that both open source and software com-mercial can be used, which ratifies the status quo (open source operat-ing systems [Linux or OpenVMS], databases [MySQL], and source code management [SubVersion]). In many IT departments and research groups open source software is used and often this use can be extensive. Some people may argue that open source software is hacked code, but when the code can be reviewed by many programmers it may be argued that the quality of the finished application could be better than some commer-cially available software. Regardless of the debate, the word established allows the use of open source applica-tions within category 1.

The second subcategory is infra-structure software tools that comprise

Table II: Modifi ed software classifi cation

Modifi ed Software Category Software Scope

Category 1: Infrastructure software • Established or commercially available layered software including operating systems.

• Infrastructure software tools.

Category 2: Firmware • Nonconfi gurable instruments• Firmware calculations such as balances (with limited risk for

changing results and data).• Standard functions of programmable fi rmware instruments

(user-defi nted programs treated as category 5).

Category 3: Nonconfi gured products • Nonconfi gured commercial software applications.• Instruments controlled by nonconfi gured software.• Instruments controlled by confi gurable software that is used

unconfi gured or with standard defaults.• Excel templates using basic arithmetic functions.

Category 4: Confi gured products • Confi gured products provide standard interfaces and functions that enable confi guration of the application to meet user-spe-cifi c business processes.

• Confi guration using a vendor-supplied language should be handled as custom components (category 5).

• Complex Excel templates or statistical calculations.

Category 5: Custom modules and applications • Custom applications.• Custom modules written either in an established computer

language or an internal scripting language that integrates with a commercial application.

• Macros for use with a commercial application.• User-defi ned fi rmware programs.• Custom fi rmware• Spreadsheet visual basic for application (VBA) macros.


a wide variety of software, such as⦁ network monitoring software ⦁ anti-virus ⦁ backup ⦁ help desk⦁ IT configuration management

tools and other network software.

While sounding like a shopping list, it provides the IT group with tools to establish, protect, monitor, and manage their computing envi-ronment and networks and where you store your laboratory data and electronic records. However, care needs to be taken with applications in this subcategory as the use of the application could drastically change the category to which it is allocated. Take for example help desk or con-figuration management applications. If no regulatory data are held exclu-sively here then they are category 1, but if they are your only tools for problem management or change con-trol processes for regulated applica-tions, then this changes the category and the validation group comes gal-loping round the bend.

From the laboratory, audit, and inspectional perspectives, what is re-quired is control of the applications that comprise group 1. Some of the typical controls will be⦁ identification of the software

(name, version, and supplier), ⦁ where is it installed including

path to the server–virtual server, ⦁ configuration to operate in your

environment, ⦁ demonstration that the software

has been installed correctly, and⦁ a simple demonstration that the

software works. All should be done regardless of

where the software originates: open source or commercial software. Furthermore, change control and configuration management are es-sential elements of control both with category 1 and the other software categories. So we qualify these items of software, not validate them. In contrast, we validate software in cat-egories 3, 4, and 5. Note that I have omitted category 2 software; we will now discuss this in more detail.

Category 2: Ignore Discontinuation of Firmware Classification — but with CareAs you can see in Table I, GAMP 4 had five categories of software, which is reduced to four in the latest version. The category that has gone missing in action is category 2 (firmware).

The argument for this category’s

discontinuation from the GAMP Forum is that firmware, which can vary from simple to custom software, can be accommodated in the other categories depending on its nature. To understand why this category was eliminated from GAMP 5 we need to consider what we mean by the term firmware. In its original form, firm-ware was a set of operating instruc-tions for an instrument embedded in a read-only memory (ROM) chip or used to start more complicated pro-grams on an instrument or device. An alternative term favored by IBM is microcode. This is software but instead of being delivered on a disk or USB stick, it comes preinstalled on a chip having been built into the instrument during the manufactur-ing process.

FOR: Firmware is still software, and different chip versions will be produced over time due to bugs that will be found and fixed by a manu-facturer. When this occurs, a new version of the firmware ROM is produced. During a maintenance

The biggest issue

with firmware

now is the

ability to define

programs or

parametrization

to produce user-

defined routines

that can be kept

in memory and

recalled at will.


visit by a service engineer (aka the Angel of Death), the firmware ROM may be replaced with a new one. Therefore, change control must include firmware as the upgraded chip may contain new functions not present in the existing chip as well as the bug fixes for the original soft-ware. One instance where a firmware change may need to be upgraded is when you install a new version of instrument–data system applica-tion software. It is possible that the drivers for your spectrometer may have changed and to ensure that the new software works correctly, the firmware needs to be upgraded at the same time. You need to know that firmware will be upgraded in advance so you can complete the change request before the work starts.

Over time, firmware chips have become bigger to allow more instruc-tions to be input and the ROM can be replaced with flash ROM, which can be erased by UV light and rewritten through a firmware updater. Other forms of firmware can be upgraded via a download; the best example of this is the BIOS firmware chip in your PC, which typically can be up-dated online to fix errors, improve functionality, and keep it current.

The biggest issue with firmware now is the ability to define programs or parametrization to produce user-defined routines that can be kept in memory and recalled at will. A typi-cal example is a dispenser–dilutor, in which a user can prepare differ-ent routines for different analytical

methods. So instead of a simple set of instructions there is now the abil-ity of the users to change the way the instrument works. However, f lexibil-ity comes at a potential price of the impact on the results and errors if the user-defined program is incorrect. The parallel in the conventional soft-ware world is Excel and the ability to produce incorrect templates or mac-ros if proper controls are not in place.

Therefore, the rationale for the dis-continuation in GAMP 5 is that the software sitting on the firmware can be classified in the remaining software categories. In doing so it should elimi-nate the impact of user-defined pro-grams that are possible with the more complex type of firmware systems.

AGAINST: Now let’s look at the argument for retaining category 2 software and ignoring the GAMP 5 advice. Looking around the labora-tory you’ll see many common labora-tory instruments (such as balances, pH meters, and dispenser dilutors) that still use a firmware chip to op-erate the instrument. Furthermore, the firmware cannot be changed by the user and the only way for a chip update is via the service engineer. Under these circumstances I believe that there is no need for excessive validation, and these instruments can still be qualified.

However, we now run into a slight problem. The issue is that the GAMP 5 approach is now in direct conflict with USP <1058> for analytical in-strument qualification (AIQ) (3), which we discussed in detail in the last “Focus on Quality” column (4).

GAMP 5 says validate and USP <1058> says qualify. Oh dear, what shall we do?

This is a common problem when different professional groups develop guidance; the individual participants sit in a silo and fail to consider any-thing outside their own boundaries. Hence, when each new guidance is unveiled with a fanfare and the chink of a cash register, it is not until later that we find that due to profes-sional myopia there are conflicts and problems. Ironically, GAMP 4 was congruent with USP <1058> and these instruments would be classified as category 2 software qualified to demonstrate their intended purpose, a simpler and quicker process than validation. Under this approach the instrument’s software is implicitly validated as part of the qualification to demonstrate intended use — job done. Why do more?

POTENTIAL SOLUTION: So how do we resolve this situation? Which takes precedence — a USP general chapter or the GAMP guide? I don’t think we need to phone a friend or go 50:50 to answer this question do we? So in the laboratory you’ll want to ignore GAMP 5 and retain category 2 software for laboratory instruments and be consistent with USP <1058>. This approach will also make your life easier in these instances.

WARNING! Implement this ap-proach with some caution and the application of intelligence. The rea-son for the warning is that most of the laboratory instruments in this section are indeed category 2 soft-ware, but some instruments can have additional functionality that needs further control (for example, theycan have programmable firmware for user-defined procedures that auto-mate tasks over and above the basic functionality of the instrument). The ability of the users to change the function of the instrument has many advantages but also comes with a significant downside: errors and incorrect procedures. Therefore each laboratory needs to take this into consideration when deciding

Some further background reading on Wikipedia

General overview of softwarehttp://en.wikipedia.org/wiki/Software_%26_Programming

Category 2: Firmware http://en.wikipedia.org/wiki/Firmware

Category 3: Nonconfi gured softwarehttp://en.wikipedia.org/wiki/

Commercial_off-the-shelf

Category 5: Custom softwarehttp://en.wikipedia.org/wiki/

Custom_softwarehttp://en.wikipedia.org/wiki/Bespoke

Confi guration managementhttp://en.wikipedia.org/wiki/

Confi guration_control


the software category and hence how much qualification or validation work is undertaken.

Therefore you’ll need to adopt a two-stage process if you develop user-defined procedures. ⦁ First, qualify the basic operation

of the dispenser–dilutor as cat-egory 2 software and undertake any calibration as necessary to ensure that the instrument is fit for its basic intended use.

⦁ Second, any user-defined pro-cedures must be documented and validated (specified and tested) as separate activities. This user-defined software is category 5 but can be validated on a simpler life cycle model, as the development platform (that is, the instrument) has already been qualified.

As you move away from simple firmware that is implicitly tested as you qualify the instrument, you will need to take this two-stage process approach, although control of user-defined procedures can be controlled by an SOP rather than writing a vali-dation plan every time you develop one of these user-defined proce-dures. You will also need to control the versions of each of these proce-dures and introduce change control to ensure that changes to each one are managed effectively.

The other issue that you may en-counter with some instruments in this category is the incorporation of calculations in the operation of an instrument. A balance is the classic example here if it is being used for content uniformity testing. Any cal-culations used as part of YOUR use of the instrument should be checked as part of the qualification to ensure compliance with the requirement of 21 CFR 211.68(b) (. . . Input to and output from the computer or related system of formulas or other records or data shall be checked for accuracy. The degree and frequency of input–output verification shall be based on the complexity and reliability of the computer or related system . . .) (6). However, the calculation checks and testing should be integrated into the

overall instrument qualification and not become a separate calculation validation in a multilayered valida-tion approach.

Software Silos or Software Continuum? The next three software categories we will discuss are intended as a continuum rather than discrete silos, so some interpretation may be neces-sary as to which category a system falls into. This will need to be docu-mented in your system risk assess-ments or validation plans, so “think things through” is the take-home message.

Category 3 Software: What’s in a Name?Category 3 in GAMP 5 has been renamed from Standard Software to Nonconfigured Product to sharpen the difference between this and cat-egory 4 software. Now this means that software that is used as installed falls into category 3 and may (note the careful use of the word may) also include software that is configurable (category 4) but is used either uncon-figured or with the standard defaults provided by the software supplier.

Despite the name, category 3 soft-ware is also configured, but for the environment (run-time configura-tion). It is this fact that distinguishes category 3 from category 4 software. What is run-time configuration? ⦁ First, is that upon installation

The major

difference

between category

3 and 4 software

is the ability

to modify the

function of the

software to

match a business

process.


of a category 3 application, the software is capable of operating and automating the business process without any modifica-tion — in fact, as noted in Table I in the GAMP 5 column, it can-not be changed in this respect. Some other terminology used to describe this type of applica-tion is canned software or com-mercial-off-the-shelf software (COTS), or even off-the-shelf software (OTS), but these are badly abused terms that can be misused to mislead or lie to users and therefore will not be used in this column.

⦁ Second, run time configuration is only the definition of items in the software to enable the system to operate within the installed environment. Some typical run time configuration parameters are the definition of users and user types for autho-rized individuals, entry of the department or company name into report headers, selection of units to present or report data, default data storage location (either a local or network direc-tory), and the default printer. Reiterating the statement above, the key characteristic of soft-ware in this category is that run time configuration does not change the automation of the business process or the collec-tion and analysis of the data and records generated by the soft-ware. This is in contrast with category 4 software, in which the actual operation of the soft-ware to support the business process is changed to match the laboratory business process.

As well as a simpler life cycle, which we will discuss in a later col-umn, the software vendor or suppli-er’s work in software development can be used to save validation effort within the laboratory, as functional and design specifications are not ex-pected from the user. Note that this life cycle does not absolve the user from defining his requirements and also demonstrating intended, use

but the testing is only a single phase. This change helps practitioners in the field to interpret software better: you could have the same software in category 3 or 4, depending whether the default settings are used or if the application is configured re-spectively. To illustrate this point, I have been involved in validating the same software application that was category 3 software when operating a time-of-f light mass spectrometry (TOF-MS) system (here the ap-plication was used as installed) but category 4 when used for bioanalysis with quantitative analysis using a triple-quadrupole MS instrument (here electronic signatures were con-figured for use in the application).

Category 4: Configured Products RefinedThe name of this category has changed to help refine and redefine the software categories; the guide has moved from defining software as “package” in GAMP version 4 to “product” in GAMP version 5. I believe this is to emphasise the com-mercial nature of category 3 and 4 software, which constitutes the bulk of the software used in laboratories today.

The major difference between cat-egory 3 and 4 software, as mentioned earlier, is the ability to modify the function of the software to match a business process. The user has the means and knowledge to change the functionality of the device in a way that changes the results outputted by

the device. As a direct consequence, this triggers increased validation ef-fort. There are many ways to achieve this but the essence is to take stan-dard software modules that provide the basic functionality to automate a process and change it by configura-tion tools. These tools are provided by the vendor of the product, hence configuration rather than using an external language to write custom code that is attached to the product. However, these tools can vary in their nature from simple configura-tion buttons that turn a feature on or off to graphical drag-and-drop to a modular “configuration” language that typically writes large blocks of software; hence, custom code, which raises the debate of configuration versus customization.

Understanding the difference be-tween configuration and customiza-tion is the key to managing software and validation risk. However, many in the laboratory are seduced by sup-pliers marketing literature that talks of configuration when in reality it is customization, as we’ll discuss now. The main point I would like to make is caveat emptor — buyer beware . . . . The user is responsible under the regulations, and if you are seduced by the marketing literature, it’s your problem.

Category 4 and 5 Software: Configure versus Customize — Where Is the Line?Configuration and customization of software are terms that are poorly defined in the validation world and frequently used interchangeably, especially in a vendor’s marketing literature. It is important to under-stand the difference between these two terms as they mean entirely different things and consequently can have a dramatic impact on the amount of validation work that you could undertake.

The problem is that even the FDA and GAMP have not been able to de-fine these terms, as configuration and custom are missing from the glos-saries from the FDA (5) and GAMP 5 (1). However, an issue arises when

Understanding

the difference

between

configuration and

customization

is the key to

managing

software and

validation risk


a “configuration” or scripting lan-guage is provided by a software ven-dor to enable the modification of a program’s function to fit the business process. Regardless of terminology, this language writes custom code within the application. The problem is a vendor’s marketing department has typically cottoned on to the idea that custom code is a bad idea and decided to call this “configuration” instead, or worst of all, COTS soft-ware — without defining what the latter term means.

Note what GAMP 5 says on this point in Appendix M4 (1): custom software components . . . , developed with an internal scripting language, written or modified to satisfy spe-cific user business requirements, should be treated as Category 5. In plain English this means that the so-called configuration is really cus-tomization. Therefore, any definition of customization needs to include the problem of the internal language customizations. So, here is my at-tempt at defining these two terms. ⦁ Configuration: The modification

of the function of a software product to meet business process or user re-quirements using tools supplied by the supplier. These tools can include input of user-defined text strings for drop-down menus, turning software functions on or off, graphical drag-ging and dropping of information elements, and creation of specific reports using the standard function-ality of the package. ⦁ Customization: The writing

of software modules, scripts, pro-cedures, or applications to meet business requirements. This can be achieved using an external pro-gramming language (such as C++ or Visual Basic for Applications or PL*SQL for database procedures), macro instructions, or an internal scripting language specific for a commercial application.

The two definitions discussed are very important, as you will have to determine if the software you have is customized or configured. Getting it wrong can result in generating the wrong data or receiving a noncom-

pliance during an inspection, as we will discuss in the next section.

Category 5: Custom Macros, Modules, and Applications Not much has changed in GAMP 5 from this apart from the name change from custom software to cus-tom application. This is the highest risk software, as it may be unique and may not be subject to the same rigors of specification and testing as com-mercial products because it could be undertaken in-house or outsourced to a commercial software company.

However, does the name of this category really reflect the situation? The problem with this category name is the use of the word applica-tion. This implies a single application but this does not reflect the whole re-ality. As GAMP 5 notes (1), software is a continuum and therefore you can have a configurable product that has custom software or custom modules written to aid functionality of the installed application. A LIMS is a case in point here; there is a language provided by the LIMS vendor to change the software function — this is category 5 software as discussed in the previous section and the output is custom code. Note that the LIMS marketing people will have you think that it is configuration. No, it is cus-tom software as it uses an internal scripting language.

This software category implicitly includes macros written say within a spectrometer application to produce a shortcut for processing or manipu-lating data; these macros are custom software and not an application per se. Each macro needs to be validated and controlled to ensure that it does what it is supposed to do. Also, we have in this software category the user-defined programs written on category 2 instruments; again each will be validated.

Therefore each application, mod-ule, user-defined program, or macro needs to be specified, version con-trolled, built, and tested (including integration testing with the com-mercial application, as applicable) as a minimum to ensure the quality

www.spec t roscopyonl ine .com

of the software. Furthermore, the custom code modules will need to have their source code managed and backed up to prevent overwriting or loss, respectively.

Users and the Software Screwup Factor Let us return to the discussion on software classification from a slightly different perspective. At this point I would like to introduce you to a rather vile and obnoxious four-let-ter word. The word is user. We now enter the realm of the user (defined as either someone in the laboratory or IT — I’m not fussy here) who has a multitude of possibilities to screw up software and also the analytical results generated by it. So we’ll take another look at the GAMP software categories from the perspective of the ability of the user to change the oper-ation of software and hence influence, manipulate, or screw up the results. ⦁ Category 1: The ability of users

to influence results with infrastruc-ture software is the lowest of all five software categories. Software in this category can evolve through patch-ing, service packs, and the occasional new version but there is not valida-tion in the classical sense. So instead of validating a steady state, one qual-ifies each upgrade. Furthermore, the layered structure here ensures that there is no or only an isolated impact on the data due to bugs or mistakes. So the ability of the users to influ-ence results is typically limited to “does the software work or not.”⦁ Category 2: Traditional firm-

ware is similar to category 1 software in that the instrument’s operations are fixed and the users cannot change the functions. Therefore, the screwup factor is relatively low and hence the risk to the data. However, user-defined programs are different and will be discussed under category 5, as there is a larger risk for incor-rect data generation.⦁ Category 3: With category 3

software we start to enter specialized rather than general software applica-tions. Here software is used virtually as installed with only the run time

configuration that can be changed by the user. As there are relatively few changes to be made to the software — perhaps where the data and re-cords are stored being the most criti-cal — the risk is greater than the last two categories but still relatively low.

⦁ Category 4: In this category there is a wider spectrum of con-figuration tools available to the users — both trained and un-trained in their use — and hence the screwup factor rises accord-ingly. With the most configu-rable systems here the risk rises to a medium level. However, with more sophisticated config-uration tools the risk increases, as more mistakes can be made. Alternatively, to reduce the screwup factor, use the software with as many default settings as practicable.

⦁ Category 5: Here is the soft-ware category in which the user screwup factor is at its highest and the impact of errors the greatest. There must be proper and effective controls in place for the overall validation of the macros, modules, and ap-plications to prevent users from doing anything stupid. However, useless users can be told by clue-less management to cut corners in the name of speed and ef-ficiency and not to fully specify or test the software. As custom software can contain data han-dling routines, these could act unpredictably or errors could be introduced that change data and

records in subtle and undocu-mented ways that have not been considered due to poor specifica-tions. Hence the need for control of the overall process.

Let’s summarize the impact of users on software. The further down the software classification you go, the software becomes more specific in function coupled with the ability of the software to directly influence the data and calculations and hence the final results. Or in other words, the ability of the users to screw up the software and the data increases as you get further down the software categories. Therefore, controlling and regulating the ability of users to configure or customize software is the key here. From a personal per-spective, avoid writing any software unless there is a good business case for doing so. My rationale is that your function in the laboratory is analytical science, not software de-velopment.

A Modified Software

Classification

So if you have been following the discussion so far we need to revise the GAMP 5 software categories to take account of the world in the laboratory. Presented in Table II is my modified classification of revised software categories, which is a com-bination of GAMP 4 with GAMP 5 classifications for the laboratory that is intended to be pragmatic and us-able while managing risk effectively.

First, we return to five categories of software but use or modify, in one case, the GAMP 5 titles for them. The five categories are shown in Table II together with the types of software examples found in each one.

Second, let us review each of the categories to see if and how they have been modified:⦁ Category 1: Use existing GAMP

5 classification — no changes proposed.

⦁ Category 2: Firmware — this category has been reinstated for many laboratory instruments to make the software classification congruent with the qualifica-

Controlling and

regulating the

ability of users

to configure

or customize

software is the

key here.



www.spectroscopyonline.com/mcdowall

tion approach in USP <1058> for Group B instruments. However, care must be taken with some systems that allow user-defined programs to be written; these must be validated and controlled separately in addition to the basic instrument qualification.

⦁ Category 3: Use existing GAMP 5 classification — no changes proposed.

⦁ Category 4: Use existing GAMP 5 classification — no changes proposed.

⦁ Category 5: Modify the name to Custom Modules and Applica-tions and expand the scope to be more explicit regarding what constitutes custom software, es-pecially in a laboratory environment.

SummaryWe have reviewed the GAMP 5 software categories and highlighted where there are strengths in the changes but also noted the problems with some of the categories from

the laboratory perspective. I have also suggested an alternative clas-sification that would benefit the laboratory and reinstate category 2 software. As with all my writing, the ideas and suggestions in this column are to get you to think, adopt, adapt, or reject as you see fit.

AcknowledgmentI would like to thank Lajos Hadju for his very helpful review and construc-tive comments during the prepara-tion of this column. It was his input on the software classification and the impact of the users that has im-proved the content of this column.

References(1) Good Automated Manufacturing

Practice Guidelines version 5, Inter-

national Society for Pharmaceutical

Engineering, Tampa, FL, 2008.

(2) Good Automated Manufacturing

Practice Guidelines version 4, Inter-

national Society for Pharmaceutical

Engineering, Tampa, FL, 2001.

(3) United States Pharmacopoeia Inc,

<1058> Analytical Instrument Quali-

fication.

(4) R.D. McDowall, Spectroscopy 24(4),

20–27 (2009).

(5) Food and Drug Administration, Glos-

sary of Computerized System and

Software Development Terminology,

1995.

R.D. McDowall

is principal of McDowall

Consulting and director

of R.D. McDowall

Limited, and “Questions

of Quality” column editor

for LCGC Europe,

Spectroscopy’s sister magazine. Address

correspondence to him at 73 Murray

Avenue, Bromley, Kent, BR1 3DJ, UK.


Current Status of Standoff LIBS Security Applications at the United States Army Research Laboratory The United States Army Research Laboratory (ARL) has been applying standoff laser-induced breakdown spectroscopy (LIBS) to hazardous material detection and determination. We describe several standoff systems that have been developed by ARL and provide a brief overview of standoff LIBS progress at ARL. We also present some current standoff LIBS results from explosive residues on organic substrates and biomaterials from different growth media. These new preliminary results demonstrate that standoff LIBS has the potential to discriminate hazardous materials in more complex backgrounds.

Frank C. De Lucia, Jr., Jennifer L. Gottfried, Chase A. Munson, and Andrzej W. Miziolek

The United States Army Research Laboratory (ARL) has been investigating the potential of standoff laser-induced breakdown spectroscopy (LIBS) for detection

and discrimination of hazardous materials for several years. The detection of small amounts of hazardous materials at a standoff distance is of great interest to many government organizations and also to industry. LIBS has several attrac-tive characteristics for future field use — real time detection, simple experimental components, and no sample preparation is required (1–7). LIBS is an atomic emission technique that in-volves focusing a pulsed laser beam to produce a microplasma on the target surface. A small amount of the target material is ablated and ionized by the plasma, leading to the genera-tion of atomic–ionic emission in the plasma during cooling. A typical LIBS experiment involves a pulsed laser source, op-tics that focus the laser pulse to a sufficient energy density to generate the microplasma, optics to collect the emission from the plasma, and a spectrometer to resolve the emission into a spectrum that is analyzed by a computer. LIBS spectra comprise atomic emission peaks and some molecular emis-sions. Because LIBS is an optical technique, the focusing and collection optics can be configured for standoff operations up to tens of meters (8–15).

Early LIBS work at ARL involved using laboratory instru-ments to collect LIBS spectra from a variety of hazardous materials, including explosives and chemical and biological warfare agent surrogates (16–19). Other groups have also collected LIBS spectra of explosives (20,21) and biomateri-als (22,23) with varying levels of subsequent data analysis. Methods to discriminate between hazardous residues and in-terferents needed to be developed. For example, the strategy for detection and discrimination of explosives is to track the elements of the oxidizer (usually an oxide of nitrogen compo-nent) relative to the fuel (usually a hydrocarbon component)

(18,34). Most explosive material will have more oxygen and nitrogen relative to carbon and hydrogen compared with non-explosive materials. In a similar manner, the major and minor elements of biological and chemical warfare agent surrogates are tracked and used to differentiate from potential interfer-ent compounds (19,35–38). Air entrainment in the plasma can interfere with explosives detection (and biological and chemi-cal agent detection to a lesser extent). Because we are tracking oxygen and nitrogen relative to carbon and hydrogen to de-termine if the sample is an explosive, the oxygen and nitrogen from the atmosphere will also contribute to the nitrogen and oxygen emission intensity. Thus, the true amounts of oxygen and nitrogen relative to carbon and hydrogen within a given sample will be obscured. As a solution, we began to use double-pulse LIBS, which involves spatially overlapping two collinear laser pulses and separating them temporally on the order of a few microseconds. One advantage of double-pulse LIBS is the enhancement of the LIBS signal (39–42). More importantly for explosives detection, the first pulse lowers the atmospheric oxygen and nitrogen density in the plasma, thus diminishing the effect of atmospheric oxygen and nitrogen atomic emission in the second analytical plasma (41). We demonstrated in the laboratory that double-pulse LIBS effectively reduced the air entrainment, thus giving a more accurate value of the nitrogen and oxygen to carbon and hydrogen atomic emission intensity ratios expected from an explosive sample (34).

The first standoff experiments (see time line in Figure 1) performed by ARL researchers were conducted at Yuma Prov-ing Ground (Yuma, Arizona) in collaboration with Spanish researchers and industry. The initial proof of principal work demonstrated that standoff explosive residue detection ap-peared feasible (43). Subsequently, several standoff systems were developed by ARL and are described in the “Standoff Systems” section. We have collected standoff LIBS spectra


Figure 1: Timeline for development of standoff LIBS systems at ARL.

from a wide variety of residues and bulk materials. Typically, a small amount of the residue material is spread on a sub-strate. In general, the coverage of the samples is estimated to be 10–100 mg/cm2, although this is not inclusive of all the sample coverage amounts we have investigated.

Initial work with the standoff instru-ment repeated studies performed in the laboratory at a standoff distance. We

collected double-pulse LIBS spectra of RDX (cyclotrimethylenetrinitramine), Composition-B (36% TNT, 63% RDX, and 1% wax), Bacillus subtilis (BG), a mold sample (Alternia alternata), diiso-propyl methyl phosphonate (C7H17O3P, DIMP, 96%), dimethyl methyl phos-phonate (C3H9O3P, DMMP, 99%), di-ethyl ethyl phosphonate (C6H15O3P, DEEP, 98%), diethyl methyl phospho-nate (C5H13O3P, DEMP, 98%), triethyl

phosphate (C6H15O4P, TEP, 99%), oil, diesel fuel, road dust, house dust, fin-gerprint residue, and plastic (Type V polypropylene [C3H6]n). We also con-firmed that double-pulse LIBS at a standoff distance was advantageous for explosives discrimination compared to single-pulse LIBS (36,44). To discrimi-nate between a hazardous material and a benign substance, we have employed a variety of discrimination techniques ranging from simple linear correlation to more advanced chemometric tech-niques (43–45). So far, we have found that partial least squares discriminant analysis (PLS-DA) is the most effective chemometric method for classifying the hazardous residue samples. Expanding upon these initial studies, we collected standoff LIBS spectra of another large set of bioagent simulants, interferents, and chemical warfare agent simulants (37). We used PLS-DA to achieve dis-crimination between bioagent simulants and interferents as well as the five nerve agent simulants. In addition, a combined PLS-DA model was developed that in-corporated all three classes of hazard-ous threats — chemical, biological, and explosive (CBE). In a limited study, the preliminary model yielded the follow-ing results: 100% true positives and a 2% false positive detection rate (37).

Standoff Systems at ARLPrototype ST-LIBS systems were de-veloped by ARL in conjunction with partners Ocean Optics, Inc. (Dunedin, Florida) and Applied Photonics Ltd. (Skipton, North Yorkshire, U.K.). The timeline for the development of these systems is shown in Figure 1. With each generation, significant design improve-ments have been made. The following section describes the design and capa-bilities of the standoff LIBS systems.

The first-generation system (Gen 1) incorporates a double-pulse Nd:YAG laser system (Big Sky CFR400-PIV, 1064 nm, 2 Hz, 250 mJ/ pulse, <10 ns pulse width). The lasers were chosen for their small footprint and rugged design. A commercially available 8″ Schmidt–Cassegrain telescope (Meade LX200GPS) is used to collect the LIBS emission along the same path tra-versed by the laser ablation beam. The

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Figure 2: LIBS spectra of (a) red silicone substrate, (b) red silicone with RDX residue, (c) red silicone with oil residue, and (d) red silicone with dust. Each spectrum is collected using a double pulse single shot at a 25-m standoff distance.


combined double-laser pulse is directed along the axis of the telescope by an ar-ticulating arm, enabling a full range of motion on the telescope for ease in targeting the sample. A diode laser (632 nm) illuminates the target spot coinci-dent with the main IR laser. The infra-red double-pulse beam is expanded with a simple two-lens system and is focused down range by a 3″ positive lens (f = 475 mm). Plasma light collected by the telescope is focused into a fiber optic and sent to a gated CCD spectrometer (500–900 nm) developed by Ocean Op-tics. A digital camera and wireless range finder enable remote viewing and mea-surement of the distance to the target.

Although we were able to collect spec-tra of metals and explosive residues on metals at 20 m with the Gen 1 system, we found that the less-than-ideal beam quality of the lasers in the far-field re-sulted in a weaker plasma that made obtaining LIBS spectra of organic ma-terials extremely challenging. For the Gen 2 system, therefore, a double-pulse laser source (Quantel Brilliant Twins, 1064 nm, 10 Hz, 335 mJ/ pulse, 5 ns pulse width) that provided superior beam quality (M2 < 2) and power at 20+ m was selected. As with the Gen 1 sys-tem, the two laser beams are combined before entering the articulating arm. A 14″ telescope (Meade LX200GPS) was fitted with UV-coated optics to provide greater light-gathering power com-pared with Gen 1 and full broadband (UV–vis–NIR) capability. A custom-made three-channel gated CCD spec-trometer (Ocean Optics) provides high light throughput and sensitivity from 190 nm to 840 nm. The entire system is mounted on a wheeled cart and is easily transportable. The data presented in the following section were acquired using the Gen 2 system (pictured in Figure 1). The optimal timing parameters for the system are as follows: delay time tdelay = 2 μs, integration time gate tint = 100 μs, and interpulse separation Δt = 3 μs.

A third-generation standoff LIBS system has been designed to address the issue of eye safety. A single Nd:YAG laser (Quantel Brilliant B, 1064 nm, 10 Hz, 850 mJ, 6 ns pulse width) is shifted to 1.54 μm using a CH4/Ar-filled Raman cell developed by the National Center for

Atmospheric Research (NCAR). As with the Gen 2 system, a modified 14″ tele-scope is used to collect the light emitted from the laser-induced plasma. Testing of this system is under way, but this sys-tem will be used primarily as a proof-of-concept due to power limitations at 1.54 μm. The high laser energies necessary for these standoff systems will always be

unsafe in the beam path, regardless of wavelength. However, potential damage from reflections and scatter can be miti-gated by using wavelengths such as 1.54 μm or the third and fourth harmonics of the Nd:YAG rather than the funda-mental and second harmonic of the Nd:YAG. Therefore, the area around the standoff system that must be controlled

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Figure 3: PLS-DA results for unknown residues on red silicone substrate tested against the RDX class of the model: RDX = blue square, dust = rust circle, oil = black diamond, and red substrate (blank) = red triangle. The open shapes are the sample spectra in the model and the filled shapes are the “unknown” test samples. The dashed line is the threshold for determining if a test sample belongs to the RDX class.

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Figure 4: Standoff LIBS spectra of biomaterials collected at 40 m: (a) Broadband spectra of BG in G (blue) and BG in SpBr (red); (b) close-up view of BG in G (blue) and BG in SpBr (red) to compare differences in growth media; and (c) close-up view of BG in SpBr (red) and Bt in SpBr (black) to compare differences in spore type


for laser safety is much smaller than if 1064 nm is used.

More recently, a fourth-generation standoff system has been designed and developed by Applied Photonics Ltd. and A3 Technologies LLC (Bel Air, Maryland) for recent field testing. A single Nd:YAG laser (Quantel Brilliant B, 1064 nm, 10 Hz, 850 mJ, 6 ns pulse width) and a modified 14″ telescope are used to generate and collect light emis-sion from the plasma. For this system, three Czerny–Turner spectrometers with ICCDs (Andor) are used to collect the LIBS spectra. Each spectrometer covers a portion of the LIBS spectrum. All of the standoff LIBS functions can be con-trolled from a remote desktop, including control of laser parameters, laser aim-ing, laser firing, spectrometer inputs, and data collection. In addition, several features have been automated such as autofocusing the laser, autofocusing the collection optics, and performing raster scans over a given area.

Finally, the newest generation (Gen5) standoff system developed by Applied Photonics, Ltd. was delivered to ARL in April 2009. This system is designed to work at 100+ m. It integrates all of the automation and computer control fea-tures of the Gen 4 system with two Nd:YAG lasers (Quantel Brilliant Bs, 1064 nm or 355 nm, 10 Hz, 850 mJ, 6 ns pulse width) for double-pulse operation. The Schmidt–Cassegrain telescope has been replaced by a military-spec 16″ Ritchey–Chretien telescope (RC Optical Systems, Inc., Flagstaff, Arizona). A new broad-band spectrometer (VSMS, PI-Acton, Trenton, New Jersey) with an ICCD will be used to collect the LIBS spectrum. The system will be evaluated using the same samples and discrimination meth-odology we have previously developed, but at much greater distances (>60 m).

Recent Work and Discussion

Our most recent work with explosives and bioagent surrogates has dealt with the inf luences background materials will have on the ability to discriminate residues. We have shown on simple alu-minum substrates that standoff LIBS can successfully discriminate residues with similar elemental constituents. Explo-sive residue samples have been classified

Figure 5: PLS-DA results of test samples BG and Bt in different growth media tested against (a) Bt and (b) BG classes of the PLS-DA model. BG = red square, Bt = blue circle, aluminum substrate = gray triangle. The open shapes are the model samples and the filled shapes are the test samples. The dashed line is the threshold for determining if a test sample belongs to the class or not.

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correctly 94% of the time (45). More com-plicated samples that contained simple mixtures of explosives and interferents were classified as explosives at an 80% true positive rate. Recently, we have in-

vestigated how using organic substrates rather than metallic substrates will af-fect our ability to discriminate between explosives and nonexplosive materials. The organic substrate material is ablated


and entrained in the plasma; thus, the carbon and hydrogen contained in the organic substrate will interfere with the carbon and hydrogen atomic emis-sion that originates from the explosive residue. We applied a small amount of RDX, road dust, and oil residue to a red silicone substrate to determine how well we could discriminate explosives from nonexplosives on an organic substrate. We collected 50 single-shot double-pulse LIBS spectra (at 25-m distance) of each residue on the substrate as well as the blank substrate itself. In Figure 2, a representative single-shot double-pulse spectrum of each residue on the organic substrate and the blank substrate are dis-played. We used 40 of the 50 spectra for each residue and the blank to generate a model in PLS-DA. From each single-shot spectrum, nine intensities and 20 ratios of these intensities were calcu-lated, as described in reference 44. These 29 variables were the inputs for the PLS-DA model. We calculated the model and then used the 10 remaining single-shot spectra from each residue type to test how the model performs. In Figure 3, which shows the predicted scores for the RDX class in the PLS-DA model for both the model and test spectra, all of the RDX residue on red silicone substrate test samples are above the classification threshold determined by the model and are thus classified as explosives. None of the nonexplosive residue test samples classify as explosive. These preliminary results indicate that LIBS can differenti-ate between explosive and nonexplosive residues on an organic substrate. Cur-rent work involves expanding the types of substrate materials and the number of explosive and interferent test samples to further test the PLS-DA models.

As described in the introduction, we have been investigating the use of LIBS with biological warfare agent surrogates for several years now. In fact, our first work with coupling chemometrics with LIBS data involved biomaterials (17). We also have performed an extensive study of biomaterials at a 20-m standoff range. These studies include an array of bio-logical warfare agent surrogates, other biomaterials, and interferents. We dem-onstrated the successful discrimination of the anthrax surrogate BG (2% false

negatives, 0% false positives) and ricin surrogate ovalbumin (0% false nega-tives, 1% false positives) (37). However, biological warfare agents can be grown in a variety of media. The elemental up-take of the bacterial spore will be differ-ent depending upon the growth media. Because LIBS tracks all of the elements in order to classify the material as a bio-logical warfare agent or as an interfer-ent, the growth media type could influ-ence how the bioagent is classified. We obtained two spore types, BG and Ba-cillus thuringiensis (Bt), in two growth media, G-Media (G) and Sporulation Broth (SpBr) from ChemImage (Pitts-burgh, Pennsylvania). We performed a small feasibility test on a limited sample set to observe the effect of growth media on the ability to discriminate between the two spores. We collected 10 single-shot LIBS spectra at 40 m for each spore type from each growth media and the blank aluminum substrate. In Figure 4, LIBS spectra of the biomaterials in the two growth media are displayed. In Figure 4b, there are several atomic emission lines that are only observed in the G growth media. In Figure 4c, there are observable differences in the relative intensities of the atomic emission lines for each spore type.

The goal was to be able to discrimi-nate between Bt and BG irrespective of growth media. A number of atomic line intensities and ratios of the atomic emis-sion intensities were selected as inputs into the PLS-DA model based upon our earlier work (37). Three classes were defined in the PLS-DA model — a BG class, a Bt class, and a substrate class (aluminum). Six BG in G samples and six BG in SpBr samples were used for the BG class; six Bt in G samples and six Bt in SpBr samples were used for the Bt class; and six aluminum substrates were used for the aluminum class in the model. The remaining 20 spectra were used as test samples for the PLS-DA model. In Figure 5, each combina-tion of spore type and growth media test sample matches the correct spore class regardless of the growth media.

Conclusions

Despite substantial success in the laboratory and in the handful of field

trials, standoff LIBS still needs further refinement before it can be consid-ered a fully viable instrument for field use by military or civilian personnel. However, LIBS is one of only a handful of techniques that have been shown to detect explosive signatures at a stand-off distance. Other techniques that are being investigated for standoff explo-sives detection include terahertz (THz) imaging, photofragmentation followed by laser-induced f luorescence (PF-LIF), photoacoustic spectroscopy, and Raman spectroscopy (46). THz time domain spectroscopy has been used to collect reflected absorption spectra of RDX at 30 m (47). Even at 30 m, how-ever, atmospheric water vapor obstructs the RDX signal, and the interference will increase as standoff distance is in-creased (48,49). PF-LIF can be used as a standoff technique and has been dem-onstrated under laboratory conditions to detect TNT molecules at a distance of up to 2.5 m (50,51). A variation of pho-toacoustic spectrosocpy using quantum cascade lasers as the optical source for illuminating surface adsorbed explo-sives and quartz crystal tuning forks as the detectors has been demonstrated recently at a distance of 20 m (52). All of these techniques are still confined to the laboratory and are still in the early stages of development. While these other tech-niques have been applied to the detec-tion of explosive materials at a standoff distance, to our knowledge, none have demonstrated discrimination between explosives and interferent materials at a standoff distance.

Raman spectroscopy is another op-tical technique that has demonstrated success for detecting explosive signa-tures (53). Raman can be used to obtain molecular information about the sample and does not leave an imprint, although photodegradation of the sample can occur. However, the signal intensity is weak and thus, acquisition time can be much longer relative to LIBS. Using LIBS and Raman together to provide orthogonal information is currently under investigation by several groups. The instruments conceivably can share the same laser and spectrometers while obtaining elemental and molecular information. The data can be fused from


the various instruments to increase the probability of detection and decrease the false alarm rate.

Since 2004, standoff LIBS at ARL has progressed rapidly from simply obtain-ing spectra of residues to discriminating these materials from interferents under various conditions (such as distance, substrate, and laser parameters). Our recent work discriminating explosives on an organic substrate and biomate-rials from different growth media has directly addressed potential challenges to standoff LIBS. Future work should address sample preparation as samples become more complex, model optimiza-tion for detection and discrimination of these samples, and the limits of detec-tion (LOD) for a particular application. Increased complexity of samples pres-ents a challenge for building and testing chemometric models. Consider a hypo-thetical 50/50 mixture of explosive and interferent distributed on a substrate. Numerous spectra of the mixture sam-ple must be collected in order to popu-late the model. The distribution of ex-plosive and interferent is not uniformly 50/50. Therefore, input into the model will contain spectral information from a wide range of explosive/interferent per-centages. The same will hold true for test samples. In reference 45, the misclassifi-cation rate is much higher for mixtures. Some of this can be attributed to how the explosive mixtures were prepared and sampled. Some of the plasma inter-rogations that we classified as explosive might have sampled regions with little to no explosive. For a more valid test, the actual composition of each plasma interrogation must be known, both for the test sample and for the model sam-ples. New ways of preparing samples for model building and testing must be de-veloped. The model itself is also sensitive to changing experimental parameters. As seen in reference 45, changing the number of latent variables in a PLS-DA model will influence the number of false negatives and false positives. The num-ber of classes and types of classes for a specific application can only be validated by testing with multiple iterations of test sets and adjustments to the contents of the model. As described previously, the model is only as good as the data that

have been used to build the model. We must also determine the LOD. The LOD is highly dependent upon experimental parameters, including but not limited to target material, laser energy, focusing optics, collection efficiency, and sub-strate. It will also be dependent upon the ability to discriminate between the explosive and nonexplosive. Again, sample preparation is very important for determining the LOD. There must be a known quantity of the explosive that is consumed by the plasma. How these quantities are prepared as the concentra-tion of the explosive decreases becomes an important question. Defining the LOD will be application-dependent as well as instrument-dependent.

Standoff LIBS, though still an evolv-ing technology, is expected to have significant impact in real-world appli-cations. The major attributes of LIBS, namely real-time response, no sample preparation required, and significant potential in sensitivity and specificity, are beginning to be applied at standoff distances of 100 m and beyond. In fact, due to the new generation of broadband,

high-resolution spectrometers, standoff LIBS represents a new tool for general materials analysis, whether the mate-rial is hazardous or benign. Thus, the areas of application for standoff LIBS are much broader than just military and security. In the future, the performance of standoff LIBS will be improved due to advances in components, most no-tably in spectrometers and lasers. Such component improvements coupled with the increase in commercial capacity for building such systems suggests that in the next few years there will be many ex-amples of real-world field applications.

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Spectral Studies on the Interaction of [Ru(bpy)2(BTIP)]2+ with DNA and Determination of Nucleic Acids at Nanogram LevelsThe interaction of [Ru(2, 2’-bipyridine)2(2-benzo[b] thien-2-yl-1H-imidazo[4,5-f][1,10] phen-anthroline)]2+ ([Ru(bpy)2(BTIP)]2+)

with nucleic acids in weak acidic medium is studied

based upon the measurements of resonance light scattering (RLS) and UV–vis absorbance. Intercalation, electrostatic interaction, and long-range assembly are observed between [Ru(bpy)2(BTIP)]2+ and calf-thymus DNA (ctDNA). The enhanced RLS intensity is proportional to the concentration of ctDNA in an appropriate range. Three synthetic samples are analyzed satisfactorily.

Chao Weng, Xiaoming Chen, and Changqun Cai

The investigation of ruthenium compounds that can bind coordinatively to DNA is one of the most attractive re-search fields for the development of novel nonplatinum

anticancer agents (1–3). Ruthenium complexes generally show fewer toxic side effects than platinum-based drugs and exhibit activity in cisplatin-resistant cells or in cells where cisplatin is totally inactive (4). Moreover, ruthenium has a rich redox chemistry whose complexes in oxidation states two and three are sufficiently stable and under current development as me-tallopharmaceuticals. Studies on the interaction of ruthe-nium compounds with DNA are not only able to determine the mechanism of some diseases, but also enable researchers to design new medications with high effective rates and low toxicity, filter new drugs, treat cancer, and alter anticancer medicine.

Since it was found that the RLS technique based upon a conventional fluorescence spectrophotometer could be used for macromolecular analysis (5,6), RLS has turned into one of the important methods for DNA and protein assay. Many negatively charged compounds can aggregate on the surface of protein, which results in the enhancement of the RLS signals and can be applied to protein assays (7–10). Furthermore, a method for the determination of protein based upon the de-crease of RLS was proposed (11). Many types of compounds characterized by positive charge that are contrary to DNA can be used to determine DNA. They include porphyrin and its derivatives (12–16), alkaline dyes (17–21), metal cation complexes (22–27), and cation surfactant (28,29), and some

anionic dyes can also be applied to DNA detection in the pres-ence of cation surfactant (30). Furthermore, RLS methods for the determination of DNA using zwitterionics (31) and poly-mers (32) were proposed.

Polypyridyl ruthenium(II) complexes are important com-plexes in DNA research. They not only show many interest-ing phenomena while binding to DNA (33,34), but some of them also distinguish B-DNA from Z-DNA (35). Fluores-cence methods for the determination of DNA using some polypyridyl ruthenium(II) complexes (36–39) were proposed. Ru(2, 2′-bipyridine)2(2-benzo[b] thien-2-yl-1H-imidazo[4,5-f][1,10] phenanthroline)2+ ([Ru(bpy)2(BTIP)]2+) is a new poly-pyridyl ruthenium(II) complex that was investigated by vari-ous spectrophotometric methods and viscosity measurements (40). At the same time, we first found that there are distinct enhanced RLS signals in the system. This implies that the new polypyridyl ruthenium(II) complex can be used as probe re-agents just as other probe reagents have been used. We propose a novel method to study the interaction of the ruthenium(II) complex with DNA using RLS. This method will also widen probe reagents for the determination of DNA.

In this study, a good linear relationship is obtained over a wide concentration range. A satisfactory result is obtained for the determination of synthetic samples of DNA. Compared with previous RLS methods (12–32), the proposed method features a relatively low detection limit, a wide linear range, and good precision. At the same time, the reaction mechanism and the influential factors on the variation of RLS between


was sufficiently free of protein. The con-centration of ctDNA is calculated based upon the absorption at 260 nm (50.0 mg/Lper OD). In this experiment, all working solution of DNA is 27.0 mg/L

[Ru(bpy)2(BTIP)](ClO4)2 •H2O was synthesized according to reference 40. A 2.5-mg mass of [Ru(bpy)2(BTIP)](ClO4)2 •H2O was first dissolved in 1 mL DMSO, and then double distilled water was added in a 250-mL volumetric f lask and 1 × 10−5 mol/L [Ru(bpy)2(BTIP)]2+ stock solution was obtained.

M/25 mixed acid solution was pre-pared by dissolving 2.47 g H3BO3, 2.71 mL 85% H3PO4, and 2.36 mL acetic acid in a 1000-mL volumetric flask in doubly distilled water, and then adjusting the pH with 0.2 M NaOH solution, so dif-ferent pH Brittion-Robinson (BR) buffer was obtained.

Experimental Procedure

Into a 5-mL volumetric test tube were successively added 1 mL 1 × 10−5 mol/L [Ru(bpy)2(BTIP)]2+ solution, 1 mL pH 6 BR buffer, and an appropriate volume of nucleic acid. The mixture was diluted to the mark with water and mixed thor-oughly before RLS measurements. All measurements were made at an ambient temperature. All RLS spectra were ob-tained by simultaneously scanning the excitation and emission monochroma-tors (namely, Δλ = 0 nm) from 250 to 800 nm. The intensity of RLS was mea-sured at λ = 374 nm in a quartz fluores-cence cell with slit width at 3.0 nm for the excitation and emission.

Results and DiscussionFeatures of RLS Spectra

The RLS spectra of [Ru(bpy)2(BTIP)]2+ a re s how n i n F i g u re 1 . B ot h [Ru(bpy)2(BTIP)]2+ and DNA have low RLS intensities at pH 6.0. However, the mixture of them has high RLS signals, which are due to [Ru(bpy)2(BTIP)]2+ ag-gregating on the surface of DNA. The highest RLS peak at 374 nm is selected for the determination of DNA.

In Figure 2, the absorption spectra of [Ru(bpy)2(BTIP)]2+ in region 220–700 nm are shown in the absence and pres-ence of DNA. As expected, the absorp-tion intensity of [Ru(bpy)2(BTIP)]2+ de-creases and it is a red shift after adding

Figure 1: Resonance light-scattering spectra of the [Ru(bpy)2(BTIP)]2+-ctDNA system. Conditions:

(BR) buffer pH 6; [Ru(bpy)2(BTIP)]2+: 2.0 × 10-6 mol/L; (a) [Ru(bpy)2(BTIP)]2+; (b) ctDNA(1.08 mg/

L); (c) [Ru(bpy)2(BTIP)]2++ 0.54 mg/L ctDNA; and (d) [Ru(bpy)2(BTIP)]2++ 1.08 mg/L ctDNA.

Figure 2: Absorbance spectra of [Ru(bpy)2(BTIP)]2+(2.0 × 10-6 mol/L) in the (a) absence and

(b) presence of ctDNA(1.08 mg/L) (pH 6.0).

[Ru(bpy)2(BTIP)]2+ and DNA have been investigated.

ExperimentalApparatus

A m o d e l L S -55 s p e c t r o m e t e r (PerkinElmer, Shelton, Connecticut) was used to record the RLS intensity. The ab-sorption spectra were recorded with a PerkinElmer model Lambda 25 spectro-photometer. The pH was measured with a model pHS-3C meter (Shanghai Leici Equipment Factory, China).

Reagents

All chemicals are analytical reagents of the best grade commercially available. All stock solutions were prepared in doubly distilled water.

The ctDNA was purchased from Sigma Co. (China). Stock solution of nucleic acid was prepared by dissolv-ing ctDNA in doubly distilled water. This stock needed to be stored at 0–4 °C with only an occasional gentle shake if needed. The solution of ctDNA gave a ratio of UV absorbance of 1.8–1.9 at 260 and 280 nm, indicating that the ctDNA

Wavelength (nm)

Wavelength (nm)


appropriate DNA. This clearly suggests that [Ru(bpy)2(BTIP)]2+ interacts with DNA most likely through a stacking in-teraction between the aromatic chromo-phore and the DNA base pairs. Hence, [Ru(bpy)2(BTIP)]2+ clearly binds to DNA by intercalation.

The corresponding relationships of the RLS spectra and the absorption spectra show clearly. The RLS peaks of 315 and 374 nm correspond to the ab-sorbance valley, and the RLS valley at about 452 nm corresponds to the peak of absorbance. Furthermore, the RLS in-tensity increases in the presence of DNA while the absorbance decreases. This could be elucidated with the theory of resonance light scattering (5,6).

Optimization of Conditions

pH effectFigure 3 shows the relationship be-tween the RLS intensity and solution pH of the system. The effect of pH on the RLS is investigated from pH 2.0 to 10.0. Figure 4 exhibits that the enhance-ment of light-scattering changes a great deal and reaches a maximum with pH 6.0. [Ru(bpy)2(BTIP)]2+ has low RLS signals in this range, whereas DNA has a high RLS intensity and the pH is lower than 3.5, which might be due to DNA aggregate forming large particles whose dimensions are comparable to the wavelength of UV–vis light and result in strong light scattering (41). But DNA has low RLS signals when the pH is higher than 3.5, so pH 6.0 is chosen for further research.

Effect of the [Ru(bpy)2(BTIP)]2+ concentrationThe effect of [Ru(bpy)2(BTIP)]2+ concen-tration on IRLS of the [Ru(bpy)2(BTIP)]2+-ctDNA system is investigated and shown in Figure 4. The [Ru(bpy)2(BTIP)]2+ concentration has an obvious effect on IRLS of the [Ru(bpy)2(BTIP)]2+-ctDNA system. The IRLS increases with the increasing concentration of [Ru(bpy)2(BTIP)]2+. Yet when the con-centration of [Ru(bpy)2(BTIP)]2+ is higher than 2.0 × 10−6 mol/L, the IRL-

Sof the [Ru(bpy)2(BTIP)]2+-ctDNA sys-tem starts to decrease, which could be caused by the high absorbance of large concentrations of [Ru(bpy)2(BTIP)]2+.

So 2.0 × 10−6 mol/L, which caused the maximum IRLS, is selected for the fol-lowing procedure.

Influence of the addition order of the reagent and the stabilityThree kinds of reagent addition orders are investigated, and all the measure-ments are done after the solutions have

Figure 3: Effect of pH on the IRLS value of the [Ru(bpy)2(BTIP)]2+-ctDNA system. Conditions: (BR)

pH = 6; (a) [Ru(bpy)2(BTIP)]2+: 2.0 × 10-6 mol/L; (b) ctDNA: 1.08 mg/L; and(c) [Ru(bpy)2(BTIP)]2++

ctDNA.

Figure 4: Effect of different concentrations of Ru(bpy)2(BTIP) 2+on the RLS signals in Ru(bpy)2(BTIP) 2+-

ctDNA system. ctDNA:1.08 mg/L; BR pH 6.0. B: [Ru(bpy)2(BTIP)]2+; D: [Ru(bpy)2(BTIP)]2++ ctDNA.

Table I: The effect of adding orders

ctDNA: 1.08 mg/L; [Ru(bpy)2(BTIP)]2+: 2.0 × 10−6 mol/L; BR: pH = 6.0

Number Addition order of reagents IRLS(a.u.)

1 [Ru(bpy)2(BTIP)]2+ + Buffer + ctDNA 388.99

2 Buffer + ctDNA + [Ru(bpy)2(BTIP)]2+ 327.80

3 [Ru(bpy)2(BTIP)]2++ ctDNA + Buffer 289.98

Concentration (10-6moIL-1)


been mixed for 10 min. As shown in Table I, the addition order of the reagent affects the RLS intensity of the system. The best order is the first mixed order.

The effect of the reaction time is eval-uated by determining the RLS intensity every 2 min with 1 h and shown in Fig-ure 5. From Figure 5, we know that the binding reaction of [Ru(bpy)2(BTIP)]2+ and DNA is completed after 10 min and can be stable over 1 h when they are mixed together at room temperature. So all measurements are made after they are mixed together at an ambient temperature of 25 °C for 10 min.

Effect of the ionic strengthNaCl was used to control the ionic strength of the solution. As shown in Figure 6, the RLS signals of the system remain constant at low ionic strength, while the reaction between [Ru(bpy)2(BTIP)]2+ and DNA is re-strained with the increasing of NaCl. The anion of phosphate on the back-bone is increasingly shielded by the cat-ionic ion with increasing ionic strength of the ionic strength controller (Na+), which is unfavorable for the assembly of [Ru(bpy)2(BTIP)]2+ on the molecu-lar surface of nucleic acids. So when the ionic strength is larger than 0.02 mol/L, the RLS signals decrease with the increasing ionic strength. This re-sult indicates that the interaction mode with [Ru(bpy)2(BTIP)]2+ and DNA also includes electrostatic interaction.

Influence of coexisting substancesTable II summarizes the effect of sub-stances including metal ions, amino acids, galactose, and adenine. Most of the metal ions in biological systems, such as K+, Na+, and Ca+, can be tolerated at

Figure 5: Effect of reaction time.(pH 6.0); ctDNA: 1.08mg/L.

Figure 6: Effect of ion strength on the IRLS value of the [Ru(bpy)2(BTIP)]2+-ctDNA system. BR: pH

= 6; ctDNA: 1.08 mg/L; [Ru(bpy)2(BTIP)]2+: 2.0 × 10-6 mol/L.

Table II: Interference of foreign substances

BR: pH = 6; [Ru(bpy)2(BTIP)]2+: 2.0 × 10−6 mol/L; ctDNA: 1.08 mg/L

Foreign substances Conc. coexisting

(mg/L)

Relative error (%) Foreign substances Conc. coexisting

(mg/L)

Relative error (%)

Zn(II) 20 −1.2 Mg(I) 20 −4.1

Mg(II) 20 0.7 Ca(II) 20 −3.3

BSA 20 −3.5 Thymine 20 −1.6

Glycine 20 −3.7 Glutamate 20 −0.5

Lactose 5 −5.9 Glucose 20 −2.2

Citric acid 20 −3.1 Uracil 20 1.6

Sucrose 20 −3.4 EDTA 20 5.5

Time (min)

Concentration (10-6moIL-1)


high concentrations. This result shows that this method is very useful.

Influence of denatured DNAWhen double-strand nucleic acids were converted into single-strand nucleic acids with the opening up of its double helix by incubation at 100 °C for 30 min and immediate cooling in ice-water for 10 min, the profile of the RLS spectra of the system displayed obvious change using single-strand nucleic acid com-pared with that using double-strand nucleic acid (Figure 7). This fact sug-gests that the [Ru(bpy)2(BTIP)]2+ can also bind single-strand nucleic acids and form bigger aggregations than double-strand nucleic acids. Because single-strand nucleic acids have more phos-phate framework exposed in water than double-strand nucleic acids, this result also indicates that [Ru(bpy)2(BTIP)]2+ can bind to DNA by static electric in-teraction mode.

Analytical Application Calibration

Under the optimum conditions, the de-pendence of IRLS upon the concentration of ct-DNA is determined. The analytical parameters of this method are listed in Table III. Table III also shows that there is a good linear relationship between IRLS and ct-DNA in a wide range of 0.00–1890 ng/L. The correlation between

IRLS and the concentration of ct-DNA is IRLS = 278.75C +80.59 (mg/L), with a correlation coefficient (r) of 0.9988. The detection limit of ct-DNA is 9.46 ng/mL. The relative standard deviation for the determination of ct-DNA at 1.08 mg/L is 1.5 % (n = 10).

Determination of DNA in Synthetic

Samples

With the calibration curves, the three synthetic samples constructed on the basis of the interference of foreign coex-isting substances are simultaneously de-termined under the same conditions (n = 5, parallel determination of five times). Their determination results are listed in Table IV. Table IV reveals that the values found in synthetic samples are identical to the expected ones; the recoveries are satisfactory within 95.0–104.2%.

ConclusionsThrough spectral studies on the in-teraction of Ru(bpy)2(BTIP)2+ with DNA, we know that the primary binding mode is intercalation. In ad-dition, electrostatic interaction and long-range assembly are the same im-portant binding modes. The enhanced RLS intensity is proportional to the concentration of ctDNA in the range of 0–1890 ng/mL, and then the sensi-tivity, selectivity, and linear range for the determination of nucleic acid by

Figure 7: Influence of denaturalization DNA on the IRLS value of the [Ru(bpy)2(BTIP)]2+-ctDNA

system. BR: pH = 6; [Ru(bpy)2(BTIP)]2+: 2.0 × 10-6 mol/L; a: ds ctDNA; b: ss ctDNA.

DNA concentration (mgL-1)


RLS is reported. All of this will help to establish a foundation for the further investigation of the nucleic acids with polypyridyl ruthenium(II) complexes at the molecular level, design new high resultful and low toxic medication, fil-ter new drugs, treat cancer, and alter anticancer medicine.

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Table III: Analytic parameters

Nucleic acidsLinear range

(mg/L)

Linear regression

equation (mg/L)

Limit of determination

(ng/mL)RSD (%)

Correlation coefficient

(r)

ctDNA 0.00–1.89 IRLS = 278.75C + 80.59 9.46 1.5 0.9988

Table IV: Determination of ctDNA in synthetic sample

Nucleic acids containing in

samples (mg/L)Main interference (mg/L) Found (n = 5, mg/L) Recovery (%)

0.30 (ctDNA) 20 Ca(II), 20 Fe(III), 20 DL-alanine, 20 DL-proline 0.29 96.7

0.60 (ctDNA) 20 Mg(II), 23 Al(III), 40 adenine, 20 DL-arginine 0.57 95.0

1.20 (ctDNA) 31 KH2PO4, 20 adenine, 20 Glucose, 20 Thymine 1.25 104.2

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UV–vis spectrophotometerShimadzu Scientific Instruments’ BioSpec-nano UV–vis spectrophotom-eter is designed for the quantitation of nucleic acids. The instrument reportedly requires a sample volume of 1 μL, for a pathlength of 0.2 mm, or 2 μL, for a pathlength of 0.7 mm. The sample is pipetted onto the instrument’s measurement plate. According to the company, sample measurement time is 3 s. The instrument includes dedicated software for operational functions. Shimadzu Scientific Instruments, Columbia, MD; www.ssi.shimadzu.com

Spectrophotometer sample compartmentA large-sample compartment from Hitachi is available for the compa-ny’s model U-3900 and U-3900H research-grade UV–vis spectropho-tometers. The sample compartment accessory adds the capability for the reflectance and transmission measurement of solid samples. The unit reportedly is stackable on top of the spectrophotometer system and can be used with specular reflectance accessories. According to the company, the compartment accommodates samples as large as 120 mm in diameter for transmission and 50 mm in diameter for reflectance. Hitachi High Technologies America, Inc., San Jose, CA; www.hitachi-hta.com

Silicon drift detectorThe XR-100SDD Silicon Drift Detector (SDD) from Amptek reportedly enables extremely high count rate applications with high energy resolu-tion. According to the company, the product is designed for XRF applica-tions from OEM handheld instruments to benchtop analyzers. Amptek, Inc., Bedford, MA; www.amptek.com

Raman microscopeHORIBA Scientific’s XploRA Raman microscope is designed to combine microscopy and chemical analysis for use in R&D, QA/QC, and forensic lab-oratories. According to the company, the instrument performs nondestruc-tive analysis at atmospheric conditions with no sample preparation. Software modules include a Guided Operation wizard. HORIBA Scientific, Edison, NJ; www.horiba.com/scientific

Portable Raman instrumentThe portable EZRaman-I instrument from Enwave is designed to provide approxi-mately 50 times better sensitivity than most other portable Raman instruments. According to the company, the instrument’s spectral resolution is approximately 6 cm−1 and its spectral range is 100–2000 cm−1. Enwave Optronics, Inc., Irvine, CA; www.enwaveopt.com

ATR accessoryThe GladiATR Vision attenuated total reflection device is designed to couple small-area infrared analysis with simultaneous view-ing. According to the company, the device’s diamond crystal enables analysis of thick or nontransparent samples. The accessory report-edly is compatible with most FT-IR spectrometers. Pike Technologies, Madison, WI; www.piketech.com

ATR sampling accessoryThe PenetrATR sampling accessory for angle-resolved attenuated total reflection (ATR) measurements from Rare Light is designed to work with all FT-IR spectrometers. The ATR accessory reportedly provides a highly collimated beam with a divergence that is controllable with the use of a spatial filter. According to the com-pany, the accessory also includes a nanorotation stage with a millidegree of angular resolution. Rare Light, Inc., Pleasanton, CA; www.rarelight.com

FT-IR cellABBSPEC’s #CXX cell is designed for the spectroscopy of catalysts and other applications. According to the company, the cell extends the upper temperature range of the company’s previous cell model. The FT-IR cell reportedly has optimized flow paths and ensures that the first contact between the gas stream and the catalyst is directly observed in an experiment. AABSPEC International, Dungarvan Waterford, Ireland; www.aabspec.com

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ICP catalogGlass Expansion’s 2009 catalog of products for ICP optical and mass spectrometry is designed for ease of use. The catalog is divided into color-coded sections for each ICP manu-facturer. Consumables and acces-sories for each model are listed with photos for identification. Products include nebulizers, spray chambers, torches, RF coils, interface cones, pump tubing, and other accessories. Glass Expansion, Pocasset, MA; www.geicp.com

Custom standardsCustomized inorganic and organic standards from SPEX CertiPrep are designed for customers looking for a specific component mix. Custom-ers can send the company a list of compounds, CAS Registry numbers, concentrations, desired matrix, and volume to receive a quote. The company reportedly is a certified ISO 9001:2000 facility and is accred-ited by A2LA as complying with the requirements of ISO/IEC 17025:2005 and ISO/IEC Guide 34-2000. SPEX CertiPrep, Inc., Metuchen, NJ; www.spexcsp.com

FluorometerThe MF2 (Multi-Frequency Fluorometer) from HORIBA Scientific is designed to obtain all frequency domain data in one measurement as fast as 1 ms. The instrument reportedly excites a sample with multiple frequencies simultaneously. HORIBA Scientific, Edison, NJ; www.horiba.com/scientific

Handheld material identification instrumentAhura Scientific’s TruScan handheld instrument is designed for rapid mate-rial verification and the inspection of incoming raw materials. According to the company, the instrument can be used by nontechnical staff for nondestructive testing and can test materials through plastic or glass containers. The instru-ment reportedly supports 21 CFR part 11 compliance. Ahura Scientific, Inc., Wilmington, MA; www.ahurascientific.com

Mass spectrometersThermo Fisher Scientific’s LTQ Velos and LTQ Orbitrap Velos mass spec-trometers are designed for coupling to ultrahigh-pressure liquid chro-matography systems. The former system includes a dual-pressure trap design and an API source, and the latter includes an Orbitrap mass analyzer, a higher-energy collisional dissociation cell, and a dual-pressure trap design. Thermo Fisher Scientific, Waltham, MA; www.thermo.com/velos

FT-IR microscope accessoriesFT-IR microscope accessories from JASCO are designed to be inter-faced with the company’s FT/IR-4000 and FT/IR-6000 spectrometers. The IRT-5000 microscope features dual detector capability and multi-ple objectives. The IRT-7000 microscope is a multichannel instrument for IR imaging. Both microscopes feature a “Smart Mapping” function that enables mapping analyses of a limited area without the use of an automated sample stage. JASCO, Easton, MD; www.jascoinc.com

Handheld Raman analyzersSafeInspec handheld Raman analyzers from BaySpec are designed to identify commonplace and rare chemicals through glass, plastic, trans-parent, and translucent materials. The analyzers are based on volume phase gratings and require no periodic calibration. A Windows-based handheld PC is included for qualitative and quantitative measure-ments. BaySpec, Inc., Fremont, CA; www.bayspec.com

LIBS instrumentsLaser-induced breakdown spectroscopy (LIBS) instruments from AnaLIBS are designed for qualitative and quantitative determinations of components present in samples such as active pharmaceutical ingredients, lubricant, excipients, and contaminants. Applications include environmental analysis, pharmaceutical analysis, metal-lurgy, gemology, public security, mining, and petroleum explora-tion. The instruments can be used for validated methods in collabo-ration with the FDA. AnaLIBS, Boucherville, Quebec, Canada; www.analibs.com


X-ray powder diffraction systemBruker’s D8 ADVANCE X-ray powder diffraction system enables users to switch between applications such as qualitative and quan-titative crystalline phase identification, microstructure and crystalline structure analysis, residual stress and texture investigations, X-ray reflectometry, and microdif-fraction. Bruker AXS, Inc., Madison, WI; www.bruker-axs.com

Calibration standardsHellma’s calibration standards are designed for checking spectropho-tometers according to national and international standards. The liquid filters can be used for performance verification of UV–vis spectropho-tometers in categories such as spectral resolution, wavelength accu-racy, photometric accuracy, and stray light. The filters reportedly are made with high purity solutions sealed in Suprasil quartz cells with a 10-mm light path. Solid filters are also avail-able. Hellma USA, Inc., Plainview, NY; www.hellmausa.com

Fiber-optic sample compartmentQuantum Northwest’s qpod sample compartment provides tem-perature control for fiber-optic spectroscopy. A cuvette holder in the center provides Peltier-based temperature control from −30 °C to 105 °C. The holder has magnetic stirring and a dry gas purge. Fiber-optic cables are attached to fused-silica lens systems and are inserted from the sides. Kits are available for absorbance, fluorescence, and multipurpose for both. Quantum Northwest, Inc., Shoreline, WA; www.qnw.com

Holographic polarizerSpecac’s holographic polarizers are available on barium fluoride, calcium fluoride, KRS-5, and zinc selenide substrates and are manu-factured at 2500 lines/mm. The polarizers are available in standard sizes of 25 mm or 50 mm diameter. Applications include the polariza-tion of radiation for unpolarized sources, attenuation of radiation from polarized sources, infrared spectroscopy of materials, and NIR and mid-IR thermal imaging. Specac, Inc., Woodstock, GA; www.specac.com

Lasers for Raman spectroscopyCW DPSS lasers in the visible wavelengths from Cobolt are available in 355, 457, 473, 491, 515, 561, and 594 nm at up to 300 mW. The lasers’ single-longitudinal mode operation reportedly provides low noise, wavelength stability, and spectral purity. They are mounted in a hermetically sealed package. Cobolt, Stockholm, Sweden; www.cobolt.se

Thin-film measurement toolHORIBA’s Auto SE thin-film measurement tool allows automatic analysis of thin films at the push of a button. According to the company, analysis requires a few seconds and the instrument provides a report of film thicknesses, optical constants, surface roughness, and nonhomogeneities. Features include an automatic XYZ stage, confocal imaging of the measurement site, and automatic selec-tion of spot sizes. HORIBA Scientific, Edison, NJ; www.autose.org

Raman spectrometerThe innoRAM high-resolution Raman spectrometer system from B&W Tek is designed for portable use with an integrated touch-screen computer and spectral resolution of 2 cm−1. The instrument’s CleanLaze laser and Raman probe reportedly provide near-excitation cut-on at 65 cm−1. B&W Tek, Inc., Newark, DE; www.bwtek.com

Portable Raman systemPerkinElmer’s Raman IdentiCheck portable Raman system is designed to combine the convenience of a portable, hand-triggered probe system with the performance of a laboratory instrument. The system com-prises an F2 Raman echelle spectrograph; an open electrode 1024 × 256 pixel CCD; a 785-nm, 300-mW laser that provides user-adjustable power between 100 mW and 10 mW at sample; kinematically mounted zero-alignment optics; and software for instrument control and Raman data acquisition and processing. Applications include forensic analysis, homeland security, QA/QC analysis, in-situ conser-vation analysis, and laboratory or in-field geological analysis. PerkinElmer, Inc., Waltham, MA; www.perkinelmer.com


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Hitachi High Technologies 3

Meinhard Glass Products 50

Microscopy and Microanalysis 2009 10

New Era Enterprises, Inc. 50

Nippon Instruments North America 50

Perkin Elmer Corporation 8

Pharma Laser 37

Photon Machines, Inc. 35

Photonics West 2009 11

Pike Technologies 7,50

Quantum Northwest, Inc. 4

Rare Light, Inc. 50

Shimadzu Scientific Instruments 13

Specac 25

SPEX CertiPrep CV4

SPIE Defense & Security 2009 15

Thermo Scientific (Part of Thermo Fisher Scientific) CV2

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